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Park, Gyuseok, Woohyeong Cho, Kyu-Sung Kim, and Sangmin Lee. "Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises." Applied Sciences 10, no. 17 (September 2, 2020): 6077. http://dx.doi.org/10.3390/app10176077.
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Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks—and associated environmental noise classification—exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.
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Mu, Qi, Xinyue Wang, Yanyan Wei, and Zhanli Li. "Low and non-uniform illumination color image enhancement using weighted guided image filtering." Computational Visual Media 7, no. 4 (July 23, 2021): 529–46. http://dx.doi.org/10.1007/s41095-021-0232-x.
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AbstractIn the state of the art, grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination. As these methods are applied to each RGB channel independently, imbalanced inter-channel enhancements (color distortion) can often be observed in the resulting images. On the other hand, images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring, halos, and over-enhancement. To address these problems, an improved RGB color image enhancement method is proposed for images captured under non-uniform illumination or in poor visibility, based on weighted guided image filtering (WGIF). Unlike the conventional retinex algorithm and its variants, WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component; it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization. To limit color distortion, RGB images are first converted to HSI (hue, saturation, intensity) color space, where only the intensity channel is enhanced, before being converted back to RGB space by a linear color restoration algorithm. Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination, with better visual quality and objective evaluation scores than from comparator algorithms. It is also efficient due to use of a linear color restoration algorithm.
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H K, Ravikiran, H. S. Mohana, Pooja P, Nayana C S, Dhruva D B, and Shreenidhi MA. "VQ-Codebook Enhancement using HGAPSO Algorithm." International Journal of Research Publication and Reviews 4, no. 3 (March 2023): 808–12. http://dx.doi.org/10.55248/gengpi.2023.32168.
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Sivakumar, Ramah, and Dr J.G.R. Sathiaseelan. "An enhanced constraint based technique for frequent itemset mining in transactional databases." International Journal of Engineering & Technology 7, no. 2.22 (April 20, 2018): 45. http://dx.doi.org/10.14419/ijet.v7i2.22.11807.
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Mining frequent patterns is one of the wide area of research in recent times as it has numerous social applications. Variety of frequent patterns finds usage in diverse applications and the research to mine those in an optimized way is an important aspect under consideration. So far, many algorithms had been proposed for mining frequent itemsets and each has their own pros and cons. The basic algorithms used in the process are Apriori, Fpgrowth and Eclat. Many enhancements of these algorithms are ongoing process in recent times. In this paper, an enhanced Varied Support Frequent Itemset (VSFIM) algorithm is proposed which is an enhancement of FPGrowth algorithm. Unique minimum support for each item in the transaction is provided and then mining is done in the proposed approach. The performance of the proposed algorithm is tested with existing algorithms. It is found that VSFIM outperformed the existing algorithms in both processing time and space utilization.
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Lian, Jian, Yan Zhang, and Cheng Jiang Li. "An Efficient K-Shortest Paths Based Routing Algorithm." Advanced Materials Research 532-533 (June 2012): 1775–79. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1775.
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We present an efficient K-shortest paths routing algorithm for computer networks. This Algorithm is based on enhancements to currently used link-state routing algorithms such as OSPF and IS-IS, which are only focusing on finding the shortest path route by adopting Dijkstra algorithm. Its desire effect to achieve is through the use of K-shortest paths algorighm, which has been implemented successfully in some fileds like traffic engineering. The correctness of this Algorithm is discussed at the same time as long as the comparison with OSPF.
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WANG Gaiyun, 王改云, 郭智超 GUO Zhichao, 路皓翔 LU Haoxiang, 陆家卓 LU Jiazhuo, and 张琦 ZHANG Qi. "融合遗传算法的多域值分块低照度图像增强算法." ACTA PHOTONICA SINICA 51, no. 4 (2022): 0410007. http://dx.doi.org/10.3788/gzxb20225104.0410007.
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Pozdeev, Alexandr A., Nataliia A. Obukhova, and Alexandr A. Motyko. "Algorithms for Real-Time Endoscopy Image Processing Pipeline in Clinical Decision Support Systems." International Journal of Embedded and Real-Time Communication Systems 10, no. 4 (October 2019): 39–59. http://dx.doi.org/10.4018/ijertcs.2019100103.
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A set of algorithms, taking in account endoscopic image features and computational cost for real-time realization is proposed. A noise reduction algorithm is based on determining the level of detail in an image fragment. For fragments with a different level of detail, different noise reduction filters are used. The enhancement algorithm is based on nonlinear contrast enhancement which highlights the contrast of vessels relative to the background without significant noise stressing, which is one of the main disadvantages of nonlinear enhancement algorithms. The custom color correction algorithm takes into account user preferences and provides a mean error less than 0.5% for each color coordinate. The “mosaic” synthesis algorithm gets panoramic images of low detail images with a mean stitching error less than 0.75 pix. The software realization of algorithms allows processing 4K endoscopic video with a speed of about 30 fps.
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Zhang, Su Ling. "Experimental Study of Human Fingerprint Image Recognition Analysis." Advanced Materials Research 971-973 (June 2014): 1616–19. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1616.
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respectively cited the fingerprint image preprocessing for image segmentation , demand pattern, image enhancement and binarization of several algorithms , and each algorithm were compared. Image segmentation algorithm studied in this paper , image enhancement algorithms, can be very good to complete the project requirements. Because each method has its advantages and disadvantages , and therefore use different methods to get different results after image processing .
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Wang, Yaming, Jiajun Wang, Yuanmei Wang, and Yude Dong. "Enhancement of Eyeround Images Based on an Improved Fuzzy Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (December 20, 1999): 441–45. http://dx.doi.org/10.20965/jaciii.1999.p0441.
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Eye ground images are complex, with many details and uncertainties. Conventional enhancement algorithms do not enhance these images suitably of inferior processing. S. K. Pal proposed a fuzzy enhancement algorithm with advantages, but these were compromised by slow processing and information loss. We propose a fuzzy enhancement algorithm for eyeground images introducing mapping and implementing the algorithm through table searches, significantly improving image quality and processing speed.
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M, Reshma, and Priestly B. Shan. "Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement." Biomedical and Pharmacology Journal 11, no. 3 (July 30, 2018): 1381–87. http://dx.doi.org/10.13005/bpj/1501.
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In Medical imaging, the dermoscopic images analysis is quite useful for the skin cancer detection. The automatic computer assisted diagnostic systems (CADS) require dermoscopic image enhancement for human perception and analysis. The traditional image enhancements methods lack the synchronization among contrast perception between human and the digital images. This paper proposes an optimized-Retinex (ORetinex) image enhancement algorithm to remove light effects, which is quite suitable for the dermoscopic image for clinical analysis for Melanoma. The value of global contrast factor (GCF) and contrast per pixel (CPP) is computed and compared with the traditional methods of image enhancements including contrast enhancement, CLAHE,Adaptive histogram equalization, Bilinear filtering and the proportion of GCF and CPP is found quite optimal as compare to these traditional methods.
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Barboriak, Daniel, Katy Peters, Allan Friedman, Henry Friedman, and Annick Desjardins. "NEIM-03. FEASIBILITY OF AUTOMATED ASSESSMENT OF PROGRESSIVE ENHANCEMENT ON MRI IN PATIENTS WITH NEWLY DIAGNOSED HIGH-GRADE GLIOMA USING A FEATURE-BASED ALGORITHM." Neuro-Oncology Advances 3, Supplement_4 (September 21, 2021): iv7. http://dx.doi.org/10.1093/noajnl/vdab112.024.
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Abstract BACKGROUND Approximately 50% of patients with newly diagnosed high-grade glioma (HGG) develop progressive enhancement between their post-operative MRI scan and 12 weeks after radiation and temozolomide. Inter-reader variability on the assessment of progressive enhancement in this patient group is a significant barrier in designing multi-center biomarker trials to distinguish true progression from pseudoprogression. Although enhancement segmentation algorithms have become more widely available, more automated and reproducible techniques to identify patients who develop progressive enhancement are needed to facilitate acquisition of non-standard of care biomarkers when this occurs. We explored the feasibility of using a feature-based algorithm in tandem with freely available / open source automated segmentation algorithms to identify this subset of patients. METHODS An automated algorithm using subtraction of registered segmentations to detect new areas of localized thickness of enhancement was developed. Criteria for feasibility (50% within 95% CI of percent patients identified, and sensitivity of >85% of patients assessed as progressed [P+] identified) were determined prospectively. The algorithm was implemented across five different automated enhancement segmentation techniques, then evaluated using a retrospective dataset of 73 patients with newly diagnosed HGG (age 50.8±13.2 years, 37 men, 36 women, 50 GBM, 23 Grade III). Standardized post-baseline brain tumor imaging protocol MR acquisitions were obtained on 1.5T and 3T scanners (GE and Siemens). On chart review, 53% of patients were assessed by neuroradiologists and/or neuro-oncologists as P+ (progression vs. pseudoprogression). RESULTS 50% was within the 95% CI of percent of patients identified for all five segmentation algorithms. Sensitivity was over 85% for three segmentation algorithms, with the MIC-DKFZ algorithm having highest sensitivity of 92%. For this algorithm, specificity was 77%, PPV was 81% and NPV was 90%. CONCLUSION A feature-based algorithm in tandem with open source segmentation algorithms showed preliminary feasibility for automated identification of patients with progressive enhancement.
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Hu, Kai, Yanwen Zhang, Feiyu Lu, Zhiliang Deng, and Yunping Liu. "An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization." Journal of Marine Science and Engineering 8, no. 10 (September 25, 2020): 741. http://dx.doi.org/10.3390/jmse8100741.
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The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.
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Yuan-Bin Wang, Yuan-Bin Wang, Qian Han Yuan-Bin Wang, Yu-Jie Li Qian Han, and Yuan-Yuan Li Yu-Jie Li. "Low illumination Image Enhancement based on Improved Retinex Algorithm." 電腦學刊 33, no. 1 (February 2022): 127–37. http://dx.doi.org/10.53106/199115992022023301012.
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<p>Aiming at the problems of insufficient illumination and low contrast of low illumination image, an improved Retinex low illumination image enhancement algorithm is proposed. Firstly, the brightness component V of the original image is extracted in HSV color space, and its enhancement by Single-Scale Retinex (SSR) is used to obtain the reflection component. For the edge problem caused by the estimation of illumination component, the Gaussian weighted bilateral filter is used as the filter function to maintain the edge information. Then, the saturation component S is adaptively stretched to improve the color saturation. However, different low illumination images have different contrast, and some images have insufficient contrast enhancement, so a global adaptive algorithm is introduced to modify the contrast and obtain the final image. According to the logarithmic characteristics of human vision, it can adaptively enhance the contrast of different images without over enhancement. Experimental results show that the proposed algorithm can effectively improve the visual quality of the image, the contrast is improved significantly and image edge details are protected, and objective evaluations such as average gradient, information entropy and peak signal-to-noise ratio have been improved.</p> <p> </p>
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Jia Hongbo, 贾洪博, 石蕴玉 Shi Yunyu, 刘翔 Liu Xiang, and 赵静文 Zhao Jingwen. "基于光照重映射的低照度图像增强算法." Laser & Optoelectronics Progress 58, no. 22 (2021): 2210014. http://dx.doi.org/10.3788/lop202158.2210014.
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Zhang Ke, 张珂, 廖育荣 Liao Yurong, 罗亚伦 Luo Yalun, and 程凌峰 Cheng Lingfeng. "基于改进同态滤波的红外图像增强算法." Laser & Optoelectronics Progress 60, no. 10 (2023): 1010003. http://dx.doi.org/10.3788/lop213373.
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Sunakara, Rajeev, and P. Ravi Sankar. "Comparative Analysis of Color Video Enhancment Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 4 (October 10, 2013): 2484–89. http://dx.doi.org/10.24297/ijct.v11i4.3133.
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Contrast enhancement has an important role in image processing applications. This paper presents a color enhancement algorithm based on adaptive filter technique. First, the proposed method is divided into three major parts: obtain luminance image and backdrop image, adaptive modification and color restoration. different traditional color image enhancement algorithms, the adaptive filter in the algorithm takes color information into consideration. The algorithm finds the significance of color information in color image enhancement and utilizes color space conversion to obtain a much better visibility. In the practical results, the proposed method reproduces better enhancement and reduce the halo distortion compared with the bilateral methods.
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Dhal, Krishna Gopal, Md Iqbal Quraishi, and Sanjoy Das. "Performance Enhancement of Differential Evolution by Incorporating Lévy Flight and Chaotic Sequence for the Cases of Satellite Images." International Journal of Applied Metaheuristic Computing 6, no. 3 (July 2015): 69–81. http://dx.doi.org/10.4018/ijamc.2015070104.
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Differential Evolution (DE) is a simple but powerful evolutionary algorithm. Crossover Rate (CR) and Mutation Factor (F) are the most important control parameters in DE. Mutation factor controls the diversification. In traditional DE algorithm CR and F are kept constant. In this paper, the values of CR and F are modified to enhance the capability of traditional DE algorithm. In the first modified algorithm chaotic sequence is used to perform this modification. In the next modified algorithm Lévy Flight with chaotic step size is used for such enhancement. In the second modified DE, population diversity has been used to build population in every generation. As a result the algorithm does not converge prematurely. Both modified algorithms have been applied to optimize parameters of the parameterized contrast stretching function. The algorithms are tested for satellite image contrast enhancement and the results are compared, which show that DE via chaotic Lévy and population diversity information outperforms the traditional and chaotic DE in the image enhancement domain.
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Pan, Li, and Zhao Xian Liu. "Automatic Airport Extraction Based on Improved Fuzzy Enhancement." Applied Mechanics and Materials 130-134 (October 2011): 3421–24. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3421.
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We put forward a process of automatic airport extraction based on the characteristics of high resolution remote sensing images. First, through image enhancement algorithm, the contrast of target and background is enhanced. Second, we can extract the possible airport through the algorithms of Ostu segmentation, mathematical morphology corrosion and region-labeling. Finally, combined with the geometric structure of the runway, the airfield runway can be extracted through the algorithms of edge detection, progressive probability Hough transform and line connection. Then the possible airport can be verified by the extracted airfield runway. In the process, we proposed an improved fuzzy enhancement algorithm for image enhancement. This algorithm has good effect on the image enhancement and has strong robustness. The results of the experiment indicate that the process of automatic airport extraction is robust and has the advantages of high speed and degree of automation.
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Raheja, Shama, and Vinay Kukreja. "ENHANCEMENTS IN SORTING ALGORITHMS: A REVIEW." JOURNAL OF TODAY'S IDEAS - TOMORROW'S TECHNOLOGIES 3, no. 1 (June 2, 2015): 73–82. http://dx.doi.org/10.15415/jotitt.2015.31005.
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Ojha, Muktesh Kumar, Amrita Rai, Anshuman Prakash, Priyesh Tiwari, and Dhiraj Gupta. "Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement." Traitement du Signal 39, no. 4 (August 31, 2022): 1387–97. http://dx.doi.org/10.18280/ts.390433.
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Nature-inspired algorithms are widely applied in the arena of image enhancement for various optimization purposes. To address the optimization complexities in various image enhancement approaches, nature-inspired optimization algorithms play a vital role. Cuckoo search is one of the prominent nature-inspired performance algorithms that we employed in this work for the enhancement of magnetic resonance imaging (MRI). We proposed a wavelet-based masking technique employing a cuckoo search algorithm whose masking value is corrected by gamma function for the contrast enhancement of MRI images. The cuckoo search algorithm can inevitably fine-tune the relation of nest building using genetic operatives like adaptive cusp and alteration. The proposed contrast enhancement scheme is examined quantitatively for different types of MRI images. Extensive simulation results compared with quantitative values have revealed that the traditional nest building of cuckoo search optimization is improved by adaptive gamma correction. Comparative analysis with the existing works establishes the usefulness of the proposed methodology over the other standard approaches.
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Alkhateeb, Faisal, and Bilal H. Abed-alguni. "A Hybrid Cuckoo Search and Simulated Annealing Algorithm." Journal of Intelligent Systems 28, no. 4 (September 25, 2019): 683–98. http://dx.doi.org/10.1515/jisys-2017-0268.
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Abstract Simulated annealing (SA) proved its success as a single-state optimization search algorithm for both discrete and continuous problems. On the contrary, cuckoo search (CS) is one of the well-known population-based search algorithms that could be used for optimizing some problems with continuous domains. This paper provides a hybrid algorithm using the CS and SA algorithms. The main goal behind our hybridization is to improve the solutions generated by CS using SA to explore the search space in an efficient manner. More precisely, we introduce four variations of the proposed hybrid algorithm. The proposed variations together with the original CS and SA algorithms were evaluated and compared using 10 well-known benchmark functions. The experimental results show that three variations of the proposed algorithm provide a major performance enhancement in terms of best solutions and running time when compared to CS and SA as stand-alone algorithms, whereas the other variation provides a minor enhancement. Moreover, the experimental results show that the proposed hybrid algorithms also outperform some well-known optimization algorithms.
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Singarimbun, Roy Nuary, Ondra Eka Putra, N. L. W. S. R. Ginantra, and Mariana Puspa Dewi. "Backpropagation Artificial Neural Network Enhancement using Beale-Powell Approach Technique." Journal of Physics: Conference Series 2394, no. 1 (December 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2394/1/012007.
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Abstract Machine learning algorithms can study existing data to perform specific tasks. One of the well-known machine learning algorithms is the backpropagation algorithm, but this algorithm often provides poor convergence speed in the training process and a long training time. The purpose of this study is to optimize the standard backpropagation algorithm using the Beale-Powell conjugate gradient algorithm so that the training time needed to achieve convergence is not too long, which later can be used as a reference and information for solving predictive problems. The Beale-Powell conjugate gradient algorithm can solve unlimited optimization problems and is much more efficient than gradient descent-based algorithms such as standard backpropagation. The research data used for the analysis were formal education participation data in Indonesia. To be trained and tested using the 7-10-1 architecture. The results showed that the Beale-Powell Conjugate Gradient algorithm could more quickly perform the training and convergence process. However, the MSE value of testing and performance is still superior to the backpropagation algorithm. So it can be concluded that for the prediction case of Formal Education Participation in Indonesia, the Conjugate Gradient Beale-Powell algorithm is good enough to optimize the performance of backpropagation standards seen from the convergence speed and training performance.
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Chen, Xinying, and Yihui Qiu. "Research on an Improved Adaptive Image Enhancement Algorithm." Journal of Physics: Conference Series 2560, no. 1 (August 1, 2023): 012019. http://dx.doi.org/10.1088/1742-6596/2560/1/012019.
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Abstract Aiming at the image degradation problems that occur during industrial activities and the shortcomings of traditional fuzzy enhancement algorithms in dealing with image degradation problems with high algorithm complexity and poor enhancement results. Based on the tuned tri-threshold fuzzy intensification algorithm, this paper proposes an adaptive image enhancement algorithm, which combines three kinds of degraded images in reality (i.e., images in dusty, night and foggy environments) for simulation verification, and compares the visual effects of the enhanced images and makes objective quantitative evaluation. The experimental results show that: the proposed algorithm can not only effectively improve the contrast of the image, keep the detailed information of the image, and make the image present a more natural visual effect, but also improve the quality evaluation index of the image, and make the variance value is improved by more than 5% compared with other comparison algorithms.
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Gopalakrishnan, Vithya. "Enhancement of Sales promotion using Clustering Techniques in Data Mart." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 2 (December 4, 2015): 6534–40. http://dx.doi.org/10.24297/ijct.v15i2.6934.
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Clustering is an important research topic in wide range of unsupervised classification application. Clustering is a technique, which divides a data into meaningful groups. K-means algorithm is one of the popular clustering algorithms. It belongs to partition based grouping techniques, which are based on the iterative relocation of data points between clusters. It does not support global clustering and it has linear time complexity of O(n2). The existing and conventional data clustering algorithms were n’t designed to handle the huge amount of data. So, to overcome these issues Golay code clustering algorithm is selected. Golay code based system used to facilitate the identification of the set of codeword incarnate similar object behaviors. The time complexity associated with Golay code-clustering algorithm is O(n). In this work, the collected sales data is pre processed by removing all null and empty attributes, then eliminating redundant, and noise data. To enhance the sales promotion, K-means and Golay code clustering algorithms are used to cluster the sales data in terms of place and item. Performances of these algorithms are analyzed in terms of accuracy and execution time. Our results show that the Golay code algorithm outperforms than K-mean algorithm in all factors.
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Román, Julio César Mello, Vicente R. Fretes, Carlos G. Adorno, Ricardo Gariba Silva, José Luis Vázquez Noguera, Horacio Legal-Ayala, Jorge Daniel Mello-Román, Ricardo Daniel Escobar Torres, and Jacques Facon. "Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology." Sensors 21, no. 9 (April 29, 2021): 3110. http://dx.doi.org/10.3390/s21093110.
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Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.
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GUO, YANHUI, H. D. CHENG, JIANHUA HUANG, WEI ZHAO, and XIANGLONG TANG. "CONTRAST ENHANCEMENT USING TEXTURE HISTOGRAM AND FUZZY ENTROPY." New Mathematics and Natural Computation 03, no. 03 (November 2007): 349–65. http://dx.doi.org/10.1142/s1793005707000835.
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Image enhancement is used to correct contrast deficiencies and to improve the quality of an image. It is essential and critical to extracting features and segmenting images. This paper presents a novel contrast enhancement algorithm based on newly defined texture histogram and fuzzy entropy with the ability to preserve edges and details, while avoiding noise amplification and over-enhancement. To demonstrate the performance, the proposed algorithm is tested on a variety of images and compared with other enhancement algorithms. Experimental results proved that the proposed method has better performance in enhancing images without over-enhancement and under-enhancement.
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Chen Qingjiang, 陈清江, 李金阳 Li Jinyang, and 胡倩楠 Hu Qiannan. "基于并联残差网络的低照度图像增强算法." Laser & Optoelectronics Progress 58, no. 14 (2021): 1410015. http://dx.doi.org/10.3788/lop202158.1410015.
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Zheng Shuangshuang, 郑爽爽, 卫文学 Wei Wenxue, and 徐聪 Xu Cong. "融合全变分与Gamma的低照度图像增强算法." Laser & Optoelectronics Progress 60, no. 12 (2023): 1210022. http://dx.doi.org/10.3788/lop221707.
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Zhuang, Liyun, and Yepeng Guan. "Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization." Computational Intelligence and Neuroscience 2018 (August 13, 2018): 1–13. http://dx.doi.org/10.1155/2018/3837275.
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A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.
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Stypiński, Miłosz, and Marcin Niemiec. "Security of Neural Network-Based Key Agreement Protocol for Smart Grids." Energies 16, no. 10 (May 9, 2023): 3997. http://dx.doi.org/10.3390/en16103997.
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Recent developments in quantum computing pose a significant threat to the asymmetric cryptography currently in use. Neural cryptography offers a potential alternative that is resistant to attacks of known quantum computer algorithms. The considered solution is lightweight and computationally efficient. If a quantum computer algorithm were successfully implemented, it could expose IoT sensors and smart grid components to a wide range of attack vectors. Given the lightweight nature of neural cryptography and the potential risks, neural cryptography could have potential applications in both IoT sensors and smart grid systems. This paper evaluates one of the suggested enhancements: the use of integer-valued input vectors that accelerate the synchronization of the Tree Parity Machine. This enhancement introduces a new parameter M that indicates the minimum and maximum values of input vector elements. This study evaluates the nonbinary version of the mutual learning algorithm in a simulated insecure environment. The results indicate that, while the Nonbinary Tree Parity Machine may involve some trade-offs between security and synchronization time, the speed improvement is more substantial than the decrease in security. The impact of this enhancement is particularly significant for smaller adjustments to parameter M.
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Liu, Feilu. "An overview of image enhancement dehazing algorithms." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 738–42. http://dx.doi.org/10.54254/2755-2721/4/2023411.
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This dissertation is an overview of Image dehazing algorithm that is utilized to process hazy images through certain technologies to remove the image haze occlusion and interference, improve the visual effect of image. For example, the contrast, color and detail and other aspects. The research method is literature review. The image enhancement algorithms mainly include histogram equilibrium, homomorphic filtering, wavelet transformation and Retinex method. The of these algorithms will be discussed detailly in the following sections of the article. The conclusion is that due to the error of the parameter information in the image with fog, the current defogging algorithm is still unable to achieve perfect results.
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Wang, Dong-xia, Mao-song Jiang, Fang-lin Niu, Yu-dong Cao, and Cheng-xu Zhou. "Speech Enhancement Control Design Algorithm for Dual-Microphone Systems Using β-NMF in a Complex Environment." Complexity 2018 (September 9, 2018): 1–13. http://dx.doi.org/10.1155/2018/6153451.
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Single-microphone speech enhancement algorithms by using nonnegative matrix factorization can only utilize the temporal and spectral diversity of the received signal, making the performance of the noise suppression degrade rapidly in a complex environment. Microphone arrays have spatial selection and high signal gain, so it applies to the adverse noise conditions. In this paper, we present a new algorithm for speech enhancement based on two microphones with nonnegative matrix factorization. The interchannel characteristic of each nonnegative matrix factorization basis can be modeled by the adopted method, such as the amplitude ratios and the phase differences between channels. The results of the experiment confirm that the proposed algorithm is superior to other dual-microphone speech enhancement algorithms.
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Lang, Fangnian, Jiliu Zhou, Yuan Yan Tang, and Zhaowei Shang. "A color image enhancement algorithm based on quaternion representation of vector rotation." International Journal of Wavelets, Multiresolution and Information Processing 13, no. 05 (September 2015): 1550038. http://dx.doi.org/10.1142/s0219691315500381.
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Detail enhancement of color images is required in many applications. Unsharp masking (UM) is the most classical tool for detail enhancement. Many generalizing UM approaches have been proposed, for example, the rational UM technique, the cubic unsharp technique, the adaptive UM technique and so on. For color images, these algorithms have three steps: (a) Implement the color2gray step; (b) design an extracting method of high frequency information (HFI) based on the luminance component (LC); (c) complete the enhancing process utilizing the HFI. However, using only the HFI of the LC may lose the HFI of the chrominance component (CC). This paper proposes a quaternion based detail enhancement algorithm to extract details of the color image using both of the luminance and CCs. The proposed algorithm is designed to address three tasks: (1) designing an extraction method of the color high frequency information (CHFI) based on quaternion description of the 3D vector rotation; (2) performing an effective fusion strategy of the CHFI and the gray high frequency information (GHFI); (3) designing a quaternion based measure method of the local dynamic range, based on which the enhancement coefficients of the proposed algorithm can be determined. The performance of the proposed algorithm compares favorably with many other similar enhancement algorithms. The eight parameters can be adjusted to control the sharpness to produce the desired results, which makes the proposed algorithm practically useful.
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Li, Yingchao, Lianji Ma, Shuai Yang, Qiang Fu, Hongyu Sun, and Chao Wang. "Infrared Image-Enhancement Algorithm for Weak Targets in Complex Backgrounds." Sensors 23, no. 13 (July 7, 2023): 6215. http://dx.doi.org/10.3390/s23136215.
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Infrared small-target enhancement in complex contexts is one of the key technologies for infrared search and tracking systems. The effect of enhancement directly determines the reliability of the monitoring equipment. To address the problem of the low signal-to-noise ratio of small infrared moving targets in complex backgrounds and the poor effect of traditional enhancement algorithms, an accurate enhancement method for small infrared moving targets based on two-channel information is proposed. For a single frame, a modified curvature filter is used in the A channel to weaken the background while an improved PM model is used to enhance the target, and a modified band-pass filter is used in the B channel for coarse enhancement followed by a local contrast algorithm for fine enhancement, based on which a weighted superposition algorithm is used to extract a single-frame candidate target. The results of the experimental data analysis prove that the method has a good enhancement effect and robustness for small IR motion target enhancement in complex backgrounds, and it outperforms other advanced algorithms by about 43.7% in ROC.
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Mwambela, Alfred J. "Evaluation of Image Enhancement Techniques for Electrical Capacitance Tomography Applications." Tanzania Journal of Science 49, no. 1 (March 31, 2023): 116–29. http://dx.doi.org/10.4314/tjs.v49i1.11.
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The fast generation of images in Electrical Capacitance Tomography (ECT) systems is a desirable feature for many industrial applications. Non-iterative reconstruction algorithms which qualify for this requirement generate poor-quality images. The Linear Back Projection (LBP) is the fastest non-iterative reconstruction algorithm. The challenge is to find a technique to improve the quality of images from LBP at a low computational cost. Image enhancement techniques have been investigated for improving the quality of images reconstructed from the LBP algorithm. Simulated and measured static and dynamic flow data were used in the evaluation. The performance results were benchmarked with results from the Projected Land Weber (PLW) one of the accurate iterative reconstruction algorithms. The Gompertz enhancement algorithm was found to have 3.5 times more computation cost than the LBP reconstruction algorithm and the accuracy of the iterative PLW reconstruction algorithm. This is noteworthy since the algorithm does achieve a good balance between accuracy and speed. The fact that the accuracy gained satisfies the reservoir management standards in the multiphase hydrocarbon production sector is significant in this regard.
Keywords: Electrical Capacitance tomography, Multiphase flow imaging, Maximum entropy thresholding, Gompertz distribution, Image enhancement
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Fan, Weiqiang, Yuehua Huo, and Xiaoyu Li. "Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform." Mathematical Problems in Engineering 2021 (March 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/5578289.
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A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. Firstly, the dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. Then, the discrete wavelet transform (DWT) is utilized to obtain the low frequency sub-band (LFS) and high frequency sub-band (HFS). Next, the wavelet shrinkage threshold is applied to calculate the wavelet threshold corresponding to the HFS at different scales. Meanwhile, a new Garrate threshold function that introduces adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients, and the Gamma function is employed to correct the LFS coefficients. Finally, the PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can not only significantly improve the overall brightness and contrast of the degraded image but also suppresses the noise of dust and spray and enhances the image details. Compared with the similar algorithms of STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the indicator values of comprehensive performance of the proposed algorithm are increased by 205%, 195%, 200%, 185%, 185%, 85%, 140%, and 215%, respectively. Moreover, compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments.
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Yang, Yu Xiang, and Jian Fen Ma. "Speech Intelligibility Enhancement Using Distortion Control." Advanced Materials Research 912-914 (April 2014): 1391–94. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1391.
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In order to improve the intelligibility of the noisy speech, a novel speech enhancement algorithm using distortion control is proposed. The reason why current speech enhancement algorithm cannot improve speech intelligibility is that these algorithms aim to minimize the overall distortion of the enhanced speech. However, different speech distortions make different contributions to the speech intelligibility. The distortion in excess of 6.02dB has the most detrimental effects on speech intelligibility. In the process of noise reduction, the type of speech distortion can be determined by signal distortion ratio. The distortion in excess of 6.02dB can be properly controlled via tuning the gain function of the speech enhancement algorithm. The experiment results show that the proposed algorithm can improve the intelligibility of the noisy speech considerably.
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Wang, Zhigang, Liqin Tian, Wenxing Wu, Lianhai Lin, Zongjin Li, and Yinghua Tong. "A Metaheuristic Algorithm for Coverage Enhancement of Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (May 12, 2022): 1–23. http://dx.doi.org/10.1155/2022/7732989.
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When wireless sensors are randomly deployed in natural environments such as ecological monitoring, military monitoring, and disaster monitoring, the initial position of sensors is generally formed through deployment methods such as air-drop, and then, the second deployment is carried out through the existing optimization methods, but these methods will still lead to serious coverage holes. In order to solve this problem, this paper proposes an algorithm to improve the coverage rate for wireless sensor networks based on an improved metaheuristic algorithm. The sensor deployment coverage model was firstly established, and the sensor network coverage problem was transformed into a high-dimensional multimodal function optimization problem. Secondly, the global searching ability and searching range of the algorithm are enhanced by the reverse expansion of the initial populations. Finally, the firefly principle is introduced to reduce the local binding force of sparrows and avoid the local optimization problem of the population in the search process. The experimental results showed that compared with ALO, GWO, BES, RK, and SSA algorithms, the EFSSA algorithm is better than other algorithms in benchmark function tests, especially in the test of high-dimensional multimodal function. In the tests of different monitoring ranges and number of nodes, the coverage of EFSSA algorithm is higher than other algorithms. The result can tell that EFSSA algorithm can effectively enhance the coverage of sensor deployment.
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Cheng, Yuan, Qiang Fan, Yun Shan Tang, and Wei Wei Miao. "Packet-Loss Distinguishing Based Link Adaptation Enhancement for Wireless LANs." Advanced Materials Research 403-408 (November 2011): 1859–64. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1859.
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As we know, packet losses in wireless local area networks (WLANs) are due to collisions or link errors. In this paper, a new strategy that distinguishes the cause of packet losses in medium access control (MAC) layer is introduced. Based on the strategy, link adaptation algorithms, including the payload size adaptation algorithm and the data rate adaptation algorithm, are proposed and employed in WLANs. Simulation results show that both algorithms increase system throughput significantly.
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Alyasseri, Zaid, and Rana Ghalib. "An optimization method for underwater images enhancement." Wasit Journal for Pure sciences 2, no. 2 (June 29, 2023): 340–51. http://dx.doi.org/10.31185/wjps.171.
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Underwater images suffer from absorption and scattering of light, so underwater images are blurry, while contrast, clarity, and lighting are low. To improve the quality of underwater images, a method based on the new metaheuristic algorithm, the CHIO algorithm, was proposed. In our work, we first read the images and convert the color system from RGB to HSV. Subsequently, apply the CHIO algorithm to the image, and finally convert the color system from HSV to RGB. Experiments on the standard benchmark dataset for underwater image optimization proved the effectiveness of the method, while the performance of our algorithm is better than that of the standard optimization algorithms.
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Zhang, Lifeng, Hongyan Cui, Anming Hu, Jiadong Li, Yidi Tang, and Roy Elmer Welsch. "An Improved Detection Algorithm for Ischemic Stroke NCCT Based on YOLOv5." Diagnostics 12, no. 11 (October 26, 2022): 2591. http://dx.doi.org/10.3390/diagnostics12112591.
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Cerebral stroke (CS) is a heterogeneous syndrome caused by multiple disease mechanisms. Ischemic stroke (IS) is a subtype of CS that causes a disruption of cerebral blood flow with subsequent tissue damage. Noncontrast computer tomography (NCCT) is one of the most important IS detection methods. It is difficult to select the features of IS CT within computational image analysis. In this paper, we propose AC-YOLOv5, which is an improved detection algorithm for IS. The algorithm amplifies the features of IS via an NCCT image based on adaptive local region contrast enhancement, which then detects the region of interest via YOLOv5, which is one of the best detection algorithms at present. The proposed algorithm was tested on two datasets, and seven control group experiments were added, including popular detection algorithms at present and other detection algorithms based on image enhancement. The experimental results show that the proposed algorithm has a high accuracy (94.1% and 91.7%) and recall (85.3% and 88.6%) rate; the recall result is especially notable. This proves the excellent performance of the accuracy, robustness, and generalizability of the algorithm.
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Tan, Wei, Chao Xu, Fang Lei, Qianqian Fang, Ziheng An, Dou Wang, Jubao Han, Kai Qian, and Bo Feng. "An Endoscope Image Enhancement Algorithm Based on Image Decomposition." Electronics 11, no. 12 (June 19, 2022): 1909. http://dx.doi.org/10.3390/electronics11121909.
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The visual quality of endoscopic images is a significant factor in early lesion inspection and surgical procedures. However, due to the interference of light sources, hardware, and other configurations, the endoscopic images collected clinically have uneven illumination, blurred details, and contrast. This paper proposed a new endoscopic image enhancement algorithm. The image decomposes into a detail layer and a base layer based on noise suppression. The blood vessel information is stretched by channel in the detail layer, and adaptive brightness correction is performed in the base layer. Finally, Fusion obtained a new endoscopic image. This paper compares the algorithm with six other algorithms in the laboratory dataset. The algorithm is in the leading position in all five objective evaluation metrics, further indicating that the algorithm is ahead of other algorithms in contrast, structural similarity, and peak signal-to-noise ratio. It can effectively highlight the blood vessel information in endoscopic images while avoiding the influence of noise and highlight points. The proposed algorithm can well solve the existing problems of endoscopic images.
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Aijing, Luo, and Yin Jin. "Research on an Improved Medical Image Enhancement Algorithm Based on P-M Model." Open Biomedical Engineering Journal 9, no. 1 (August 31, 2015): 209–13. http://dx.doi.org/10.2174/1874120701509010209.
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Image enhancement can improve the detail of the image to achieve the purpose of the identification of the image. At present, the image enhancement is widely used in medical images, which can help doctor’s diagnosis. IEABPM (Image Enhancement Algorithm Based on P-M Model) is one of the most common image enhancement algorithms. However, it may cause the loss of the texture details and other features. To solve the problems, this paper proposes an IIEABPM (Improved Image Enhancement Algorithm Based on P-M Model). The simulation demonstrates that IIEABPM can effectively solve the problems of IEABPM, and improve image clarity, image contrast, and image brightness.
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Zhang, Xiaowen, Yongfeng Ren, Guoyong Zhen, Yanhu Shan, and Chengqun Chu. "A color image contrast enhancement method based on improved PSO." PLOS ONE 18, no. 2 (February 9, 2023): e0274054. http://dx.doi.org/10.1371/journal.pone.0274054.
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Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle’s flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.
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S, Siva Priyanka, and Kishore Kumar T. "Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement." Traitement du Signal 38, no. 3 (June 30, 2021): 785–95. http://dx.doi.org/10.18280/ts.380325.
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In speech communication applications such as teleconferences, mobile phones, etc., the real-time noises degrade the desired speech quality and intelligibility. For these applications, in the case of multichannel speech enhancement, the adaptive beamforming algorithms play a major role compared to fixed beamforming algorithms. Among the adaptive beamformers, Generalized Sidelobe Canceller (GSC) beamforming with Least Mean Square (LMS) Algorithm has the least complexity but provides poor noise reduction whereas GSC beamforming with Combined LMS (CLMS) algorithm has better noise reduction performance but with high computational complexity. In order to achieve a tradeoff between noise reduction and computational complexity in real-time noisy conditions, a Signed Convex Combination of Fast Convergence (SCCFC) algorithm based GSC beamforming for multi-channel speech enhancement is proposed. This proposed SCCFC algorithm is implemented using a signed convex combination of two Fast Convergence Normalized Least Mean Square (FCNLMS) adaptive filters with different step-sizes. This improves the overall performance of the GSC beamformer in real-time noisy conditions as well as reduces the computation complexity when compared to the existing GSC algorithms. The performance of the proposed multi-channel speech enhancement system is evaluated using the standard speech processing performance metrics. The simulation results demonstrate the superiority of the proposed GSC-SCCFC beamformer over the traditional methods.
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Mohsin Al-Wazni, Hazim Sadeq, and Shatha Suhbat Abdulla Al-Kubragyi. "A hybrid algorithm for voltage stability enhancement of distribution systems." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 50. http://dx.doi.org/10.11591/ijece.v12i1.pp50-61.
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This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
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Xi, Wenfei, Xiaoqing Zuo, and Arun Kumar Sangaiah. "Enhancement of Unmanned Aerial Vehicle Image with Shadow Removal Based on Optimized Retinex Algorithm." Wireless Communications and Mobile Computing 2022 (April 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/3204407.
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Images taken by UAVs have shadows due to terrain factors. The image pixel brightness of the shadow areas is compressed, and the information is deficient, which impacts the recognition of image information and thus limits the subsequent image application. Therefore, the shadow removal of the image is crucial. Image enhancement algorithm is capable of improving the whole and partial contrasts of images, highlighting detail information, and removing shadows. Three classical optical image enhancement algorithms are analyzed. The analysis results show that image would be enhanced excessively after the histogram equalization algorithm to the shadow image enhancement. The pixel brightness are compressed by the Mask homogenization algorithm enhancement and uneven brightness in some areas after the enhancement of the traditional Retinex algorithm. Using the Retinex enhancement algorithm, this study proposes a combination algorithm to remove the shadow of the UAV remote sensing image. The proposed algorithm integrates the Retinex algorithm with the two-dimensional (2D) gamma function to remove the brightness colour of the UAV image, so it is capable of removing the shadow area of the UAV image and correcting the uneven darkness attributed to the image enhancement. The acquired UAV image is used to perform the experiment, and it is integrated with the LOG algorithm to extract the enhanced image features. As indicated by the experimental results, the integrated algorithm is proved with better performance to remove the UAV image shadow. The shadow areas of the features cannot be extracted in the original image, but after using the new algorithm to remove the shadow, the ground edge features can be clearly extracted.
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Gnanamanickam, Jenifa, Yuvaraj Natarajan, and Sri Preethaa K. R. "A Hybrid Speech Enhancement Algorithm for Voice Assistance Application." Sensors 21, no. 21 (October 23, 2021): 7025. http://dx.doi.org/10.3390/s21217025.
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In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.
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Huang, Qiuhe. "An Image Sharpness Enhancement Algorithm Based on Green Function." Traitement du Signal 38, no. 2 (April 30, 2021): 513–19. http://dx.doi.org/10.18280/ts.380231.
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The traditional image sharpness enhancement algorithm faces several defects, namely, the lack of details, and the poor subjective effect. To solve these defects, this paper proposes an image sharpness enhancement algorithm based on the Green function. Specifically, the Retinex model was employed to ensure that the enhanced image has outstanding details, and the Poisson’s equation was solved to maintain the consistency between the enhanced image and the original image in the gradient domain. Then, adaptive brightness mapping was carried out to determine the boundary conditions suitable for display, and the boundary of the region was sampled to reduce the complexity of our algorithm. Experimental results show that our algorithm improved the contrast and sharpness of images from the levels of contrastive image enhancement algorithms.
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Nnolim, U. A. "Probabilistic, Multi-Scale Fractional Tonal Correction Bilateral Filter-Based Hazy Image Enhancement." International Journal of Image and Graphics 20, no. 02 (April 2020): 2050010. http://dx.doi.org/10.1142/s0219467820500102.
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This paper describes an algorithm utilizing a modified multi-scale fractional order-based operator combined with a probabilistic tonal operator, adaptive color enhancement and bilateral filtering to process hazy and underwater images. The multi-scale algorithm complements the tonal operator by enhancing edges, preventing overexposure of bright image regions, while enhancing details in the dark areas. The addition of a previously developed global enhancement operator removes color cast and improves global contrast in underwater images. The color enhancement function augments the color results of the dehazing algorithm without distorting image intensity. Furthermore, the bilateral filter suppresses noise while preserving enhanced details/edges due to the multi-scale algorithm. Experimental results indicate that the proposed system yields comparable or better results than other algorithms from the literature.