Journal articles: 'Adaptive bilateral filter'

1

Chen, Bo-Hao, Yi-Syuan Tseng, and Jia-Li Yin. "Gaussian-Adaptive Bilateral Filter." IEEE Signal Processing Letters 27 (2020): 1670–74. http://dx.doi.org/10.1109/lsp.2020.3024990.

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Kim, Hak Gu, Dong Bok Lee, and Byung Cheol Song. "Adaptive Unsharp Masking using Bilateral Filter." Journal of the Institute of Electronics Engineers of Korea 49, no. 11 (November 25, 2012): 56–63. http://dx.doi.org/10.5573/ieek.2012.49.11.056.

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Kumar, Ch Ravi, and S. K. Srivatsa. "Enhancement of Image Sharpness with Bilateral and Adaptive Filter." International Journal of Information and Education Technology 6, no. 1 (2016): 50–53. http://dx.doi.org/10.7763/ijiet.2016.v6.657.

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Gupta, Saroj Kumar, M. V. Jagannatha Reddy, and A. Nanda Kumar. "Possibilistic Clustering Adaptive Smoothing Bilateral Filter Using Artificial Neural Network." International Journal of Engineering and Technology 2, no. 6 (2010): 499–503. http://dx.doi.org/10.7763/ijet.2010.v2.171.

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Qiegen, Liu, Luo Jianhua, and Zhu Yuemin. "Adaptive Image Decomposition by Improved Bilateral Filter." International Journal of Computer Applications 23, no. 7 (June 30, 2011): 16–22. http://dx.doi.org/10.5120/2900-3798.

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Wang, Jin, Zhensen Wu, and Jiaji Wu. "Efficient Adaptive Deinterlacing Algorithm Using Bilateral Filter." MATEC Web of Conferences 61 (2016): 02021. http://dx.doi.org/10.1051/matecconf/20166102021.

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Salehi, Hadi, and Javad Vahidi. "An Ultrasound Image Despeckling Method Based on Weighted Adaptive Bilateral Filter." International Journal of Image and Graphics 20, no. 03 (July 2020): 2050020. http://dx.doi.org/10.1142/s0219467820500205.

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Images are widely used in engineering. Unfortunately, ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a critical preprocessing step. Therefore, a robust despeckling method and accurate evaluation of images are suggested. We suggest three phases and a three-step denoising filter. In the first phase, the coefficients of variation are computed from the noisy image. The second phase is a three-step denoising filter. The first step is denoising of extreme levels of homogeneous regions, based on fuzzy homogeneous regions. The second step is a proposed adaptive bilateral filter (ABF). The ABF helps for better denoising based on the three regions which are edge, detail and homogeneous regions. The next step, a weight, is applied to the ABF. This step is for isolated noise denoising. Next, in the third phase, the output image is evaluated by the fuzzy logic approach. The proposed method is compared with other filters in the literature. The experimental outcomes show that the proposed method has better performance than the other filters. That proposed denoising algorithm is able to preserve image details and edges when compared with other denoising methods.

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Salehi, Hadi, Javad Vahidi, and Homayun Motameni. "A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation." International Journal of Image and Graphics 18, no. 04 (October 2018): 1850023. http://dx.doi.org/10.1142/s0219467818500237.

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In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulations and evaluations mentioned in this paper are accomplished on some standard images such as Lena, boy, fruit, mandrill, Barbara, butterfly, and boat. Next, weaker models are omitted by studying of the various models. Establishment of new generations performs in a form that a generation emendation is carried out, and final model has a more optimum quality compared to each two filters in order to obviate the noise. At the end, the results of this system are studied so that a comprehensive model with the best performance is to be found. Experiments show that the proposed method has better performance than wavelet, bilateral, Butterworth, and some other filters.

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I., Roa, Zeinab A., and Banazier A. "Mammogram Images Enhancement using Adaptive Morphological Bilateral Filter." International Journal of Computer Applications 179, no. 27 (March 20, 2018): 45–50. http://dx.doi.org/10.5120/ijca2018916613.

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DaoNam, Anh. "Local Adaptive Bilateral Filter with Variation for Deblurring." International Journal of Computer Applications 86, no. 15 (January 16, 2014): 13–18. http://dx.doi.org/10.5120/15060-3395.

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., R. Rejitha. "MODIFIED ADAPTIVE BILATERAL FILTER FOR IMAGE CONTRAST ENHANCEMENT." International Journal of Research in Engineering and Technology 03, no. 04 (April 25, 2014): 258–62. http://dx.doi.org/10.15623/ijret.2014.0304046.

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Liu, Liu, Fang Zhou, Jing Chen, Xuezhi Yang, Lu Jia, Zhangyu Dong, and Jiaqiu Ai. "Despeckling PolSAR images with an adaptive bilateral filter." Journal of Applied Remote Sensing 11, no. 2 (June 23, 2017): 020501. http://dx.doi.org/10.1117/1.jrs.11.020501.

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K. Kannan. "Multi focused Image Fusion using Fast Adaptive Bilateral Filter." International Journal of Advanced Networking and Applications 14, no. 03 (2022): 5477–81. http://dx.doi.org/10.35444/ijana.2022.14309.

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This paper presents Fast Adaptive Bilateral Filter (FABF) for fusion of Multi Focuses images. Multi Focused image fusion is used to combine one or more input image into single composite image, focusing all objects in the given scene. FABF filter sharpens the image without producing under and over shoot by increasing the edge slope. This paper uses this property to decompose the input image into high and low frequency images so that different fusion rules can be used for high and low frequency images to produce good quality composite image. The performance this FABF filter in Multi focused image fusion is compared with Adaptive Bilateral Filter (ABF) using Root Mean Square Error (RMSE), Spatial Frequency (SF) and Mutual Information (MI).

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Wang, Hua Zhang, and Qin Zhen Huang. "SAR Image Denoising Using an Improved Adaptive Bitateral Filter." Advanced Materials Research 842 (November 2013): 672–77. http://dx.doi.org/10.4028/www.scientific.net/amr.842.672.

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Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise. The presence of speckle damages radiometric resolution, at the same time, it hampers the human interpretation and scene analysis for SAR images. On the base of studying and analyzing the mathematical model of the bilateral filter, the paper proposed a modified adaptive bilateral filter (MABF). First, it separates non-independent two-dimensional Gaussian filter into two independent one-dimensional Gaussian filter, which improves the operation speed greatly. Then through the effective noise parameter estimation, it adaptively selects optimal parameters, which improves the filtering effect. The real SAR image data is used to test the presented method and the experimental results verify that MABF is feasible and effective.

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Asuntha A and Andy Srinivasan. "Lung cancer detection using Adaptive Bilateral Filter (ABF) techniques." International Journal of Research in Pharmaceutical Sciences 10, no. 3 (July 12, 2019): 1857–60. http://dx.doi.org/10.26452/ijrps.v10i3.1383.

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Lung cancer plays a major role among the people who are affected with cancer. The major reason is the presence of nodule in a lung region. Early diagnosis of this nodule may decrease the severity also increase the life span of a patient. In this paper, a methodology is proposed to detect the lung nodule and nodule region using texture features. Various image processing techniques are used in this paper. CT images are taken as input over MRI because of its advantages over less exposure of radiation[4]. The given input image is denoised by using adaptive bilateral filter and image contrast is improved by the histogram equalization technique. Superpixel segmentation is used for the segmentation process. A Simulation process has been done using MATLAB software.

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Zhang, Yinxue, Xuemin Tian, and Peng Ren. "An adaptive bilateral filter based framework for image denoising." Neurocomputing 140 (September 2014): 299–316. http://dx.doi.org/10.1016/j.neucom.2014.03.008.

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Kala R and Deepa P. "Adaptive fuzzy hexagonal bilateral filter for brain MRI denoising." Multimedia Tools and Applications 79, no. 21-22 (April 3, 2019): 15513–30. http://dx.doi.org/10.1007/s11042-019-7459-x.

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Buyue Zhang and J. P. Allebach. "Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal." IEEE Transactions on Image Processing 17, no. 5 (May 2008): 664–78. http://dx.doi.org/10.1109/tip.2008.919949.

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Inal, Tolga, Gokce Kaan Atac, and Ziya Telatar. "Effect of Noise Adaptive Wavelet Filter on Diagnostic Performance in Stroke Perfusion." Journal of Medical Imaging and Health Informatics 11, no. 3 (March 1, 2021): 720–29. http://dx.doi.org/10.1166/jmihi.2021.3341.

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Background: Computed tomography perfusion (CTP) images include more noise than routine clinic computed tomography (CT) images. Singular value decomposition based deconvolution algorithms are widely used for obtaining several functional perfusion maps. Recently block circulant singular value decomposition algorithms become popular for its superior property of immunity to contrast bolus lag. It is well known from literature that these algorithms are very sensitive to noise. There are a lot of examples of noise reduction filters in the literature as well as commercial ones. Functional maps which help physicians in the diagnostic process can be obtained with better image quality by de-noising CTP images with adaptive noise reduction filters. Objective: In this study, the effect of a noise adaptive wavelet filtering method on diagnostic performance on CTP stroke patient images is investigated. Method: Images of acute stroke patients were de-noised by this method and their diagnostic value were evaluated by visual means, peak signal-to-noise ratio and time intensity profile metrics. An observer evaluation study was carried out in order to validate quantitative image quality metrics. The results are compared with Gaussian and a bilateral filter based filtering method called TIPS (Time Intensity Profile Similarity) on same images sets to benchmark proposed method. Results: The diagnostic value of the images obtained from noise adaptive wavelet filtering method were better than Gaussian filter method and were compatible with a wellknown time intensity profile similarity bilateral filter method. Diagnostic performance of the both observers were improved compared to both Gaussian and TIPS methods. Conclusion: The noise adaptive wavelet filter method succeeded to reduce noise while preserving details contained in the contrast bolus. Its final effect on the timeintensity profiles and generated perfusion maps are compatible with the literature and showed improvements on diagnostic performance on specificity and overall accuracy when compared to other methods.

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Wang, Jiefei, Yupeng Chen, Tao Li, Jian Lu, and Lixin Shen. "A Residual-Based Kernel Regression Method for Image Denoising." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5245948.

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We propose a residual-based method for denoising images corrupted by Gaussian noise. In the method, by combining bilateral filter and structure adaptive kernel filter together with the use of the image residuals, the noise is suppressed efficiently while the fine features, such as edges, of the images are well preserved. Our experimental results show that, in comparison with several traditional filters and state-of-the-art denoising methods, the proposed method can improve the quality of the restored images significantly.

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Dangguo Shao, Paul Liu, and Dong C. Liu. "Histogram-Based Fast Adaptive Bilateral Filter for Ultrasound Speckle Reduction." International Journal of Digital Content Technology and its Applications 6, no. 23 (December 31, 2012): 298–305. http://dx.doi.org/10.4156/jdcta.vol6.issue23.34.

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Kaur, Savroop, and Hartej S. Dadhwal. "Biorthogonal Wavelet Transform Using Bilateral Filter and Adaptive Histogram Equalization." International Journal of Intelligent Systems and Applications 7, no. 3 (February 8, 2015): 37–43. http://dx.doi.org/10.5815/ijisa.2015.03.05.

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Kwon, Koojoo, Min-Su Kim, and Byeong-Seok Shin. "A fast 3D adaptive bilateral filter for ultrasound volume visualization." Computer Methods and Programs in Biomedicine 133 (September 2016): 25–34. http://dx.doi.org/10.1016/j.cmpb.2016.05.008.

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Li, Hui, and Xu-Liang Duan. "SAR Ship Image Speckle Noise Suppression Algorithm Based on Adaptive Bilateral Filter." Wireless Communications and Mobile Computing 2022 (August 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/9392648.

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Aiming at the shortcomings of traditional bilateral filtering in suppressing speckle noise in SAR ship images, especially strong speckle noise and loss of image edge details, it proposes an improved bilateral filtering algorithm based on fast adaptive threshold and variable window in this paper. The algorithm was used to suppress speckle noise in SAR ship images. The traditional bilateral filtering cannot effectively filter out the strong speckle noise, but the SAR image has strong speckle noise because of the defects of its imaging principle. To solve these problems, an image sample truncation method based on fast adaptive truncation statistical characteristics is designed to adjust the photometric similarity weight characteristics to realize the adaptive adjustment of spatial standard deviation and gray standard deviation. After the local reference window is modified and truncated according to the local characteristics of the image, the adjusted combined similarity weight value greatly reduces the impact of strong speckle noise on the image. It is smoothed into speckle signal with strong impulse noise. In the traditional bilateral filtering, in order to enhance the effect of smoothing noise, it is necessary to specify a large value of geometric diffusion factor and gray similarity diffusion factor, resulting in the loss of image details. Based on the variable window size filtering method, when the extended local reference window is in the case of nonuniform edge, its window can be enlarged to make the speckle noise stronger. When an extended window contains details such as edges and textures, its size is not expanded to maintain image detail. This method can further smooth the speckle noise in the uniform region while preserving the edge details of the image. Finally, the adaptive truncated sample is used as the input of the bilateral filter. The image sample truncation method based on fast adaptive threshold can effectively eliminate the strong speckle noise information that affects the photometric similarity and weight accuracy of the image. The method based on variable window can greatly enhance the smoothness of the edge area of the image. The experimental results show that improved adaptive bilateral filtering algorithm improves the speckle noise removal ability by 16.06% compared with the traditional bilateral filtering algorithm in the speckle noise suppression of the SAR ship image, and the preservation performance of the image edge after filtering is improved by 5.41%. Compared with the original image, the filtered image has a 1.2% improvement in structural similarity. The algorithm can effectively suppress speckle noise and has a good ability to retain edge and texture information of the SAR ship image, which has strong practicability.

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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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Liang, Hu, Na Li, and Shengrong Zhao. "Salt and Pepper Noise Removal Method Based on a Detail-Aware Filter." Symmetry 13, no. 3 (March 21, 2021): 515. http://dx.doi.org/10.3390/sym13030515.

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The median-type filter is an effective technique to remove salt and pepper (SAP) noise; however, such a mechanism cannot always effectively remove noise and preserve details due to the local diversity singularity and local non-stationarity. In this paper, a two-step SAP removal method was proposed based on the analysis of the median-type filter errors. In the first step, a median-type filter was used to process the image corrupted by SAP noise. Then, in the second step, a novel-designed adaptive nonlocal bilateral filter is used to weaken the error of the median-type filter. By building histograms of median-type filter errors, we found that the error almost obeys Gaussian–Laplacian mixture distribution statistically. Following this, an improved bilateral filter was proposed to utilize the nonlocal feature and bilateral filter to weaken the median-type filter errors. In the proposed filter, (1) the nonlocal strategy is introduced to improve the bilateral filter, and the intensity similarity is measured between image patches instead pixels; (2) a novel norm based on half-quadratic estimation is used to measure the image patch- spatial proximity and intensity similarity, instead of fixed L1 and L2 norms; (3) besides, the scale parameters, which were used to control the behavior of the half-quadratic norm, were updated based on the local image feature. Experimental results showed that the proposed method performed better compared with the state-of-the-art methods.

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Priya, S. V., and R. Seshasayanan. "Impulse Noise Removal Using Adaptive Bilateral Filter with Robust Noise Detector." Journal of Computational and Theoretical Nanoscience 13, no. 12 (December 1, 2016): 9090–97. http://dx.doi.org/10.1166/jctn.2016.6285.

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Wang, Zhaohui. "Development of an adaptive bilateral filter for evaluating color image difference." Journal of Electronic Imaging 21, no. 2 (June 22, 2012): 023021. http://dx.doi.org/10.1117/1.jei.21.2.023021.

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Shao, Dangguo, Paul Liu, and Dong C. Liu. "Characteristic matching-based adaptive fast bilateral filter for ultrasound speckle reduction." Pattern Recognition Letters 34, no. 5 (April 2013): 463–69. http://dx.doi.org/10.1016/j.patrec.2012.12.006.

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Park, Cheul-Woo, Hyuk-Ju Kwon, and Sung-Hak Lee. "Illuminant Adaptive Wideband Image Synthesis Using Separated Base-Detail Layer Fusion Maps." Applied Sciences 12, no. 19 (September 21, 2022): 9441. http://dx.doi.org/10.3390/app12199441.

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In this study, we present a wideband image synthesis technique for day and night object identification. To synthesize the visible and near-infrared images, a base component and a detailed component are first decomposed using a bilateral filter, and the detailed component is synthesized using a local variance map. In addition, considering the difference in the near-infrared image characteristics between daytime and nighttime, the base components are synthesized using a luminance saturation region map and depth and penetration map using a joint bilateral filter. The proposed method overcomes the partial over- or under-exposure caused by sunlight and infrared auxiliary light, which is experienced variously in wideband imaging, and improves the identification of objects in various indoor and outdoor images compared with that achieved by existing methods by emphasizing detailed components.

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Gollamandala, Udaykiran Bhargava, Vasujadevi Midasala, and Vallabhuni Rajeev Ratna. "FPGA implementation of hybrid recursive reversable box filter-based fast adaptive bilateral filter for image denoising." Microprocessors and Microsystems 90 (April 2022): 104520. http://dx.doi.org/10.1016/j.micpro.2022.104520.

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Radhika, R., and Rashima Mahajan. "An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images." Measurement: Sensors 29 (October 2023): 100880. http://dx.doi.org/10.1016/j.measen.2023.100880.

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Maeda, Yoshihiro, Norishige Fukushima, and Hiroshi Matsuo. "Taxonomy of Vectorization Patterns of Programming for FIR Image Filters Using Kernel Subsampling and New One." Applied Sciences 8, no. 8 (July 26, 2018): 1235. http://dx.doi.org/10.3390/app8081235.

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This study examines vectorized programming for finite impulse response image filtering. Finite impulse response image filtering occupies a fundamental place in image processing, and has several approximated acceleration algorithms. However, no sophisticated method of acceleration exists for parameter adaptive filters or any other complex filter. For this case, simple subsampling with code optimization is a unique solution. Under the current Moore’s law, increases in central processing unit frequency have stopped. Moreover, the usage of more and more transistors is becoming insuperably complex due to power and thermal constraints. Most central processing units have multi-core architectures, complicated cache memories, and short vector processing units. This change has complicated vectorized programming. Therefore, we first organize vectorization patterns of vectorized programming to highlight the computing performance of central processing units by revisiting the general finite impulse response filtering. Furthermore, we propose a new vectorization pattern of vectorized programming and term it as loop vectorization. Moreover, these vectorization patterns mesh well with the acceleration method of subsampling of kernels for general finite impulse response filters. Experimental results reveal that the vectorization patterns are appropriate for general finite impulse response filtering. A new vectorization pattern with kernel subsampling is found to be effective for various filters. These include Gaussian range filtering, bilateral filtering, adaptive Gaussian filtering, randomly-kernel-subsampled Gaussian range filtering, randomly-kernel-subsampled bilateral filtering, and randomly-kernel-subsampled adaptive Gaussian filtering.

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Wang, Zhonghua, Siwei Duan, and Chengli Sun. "Infrared Small Target Detection Method Combined with Bilateral Filter and Local Entropy." Security and Communication Networks 2021 (February 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/6661852.

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According to the larger false alarm rate resulted from fluctuant clutter, a novel method combined with bilateral filter and local entropy is proposed for infrared small target detection in this paper. Firstly, the original image is respectively processed by bilateral filter and local entropy, and then the two processed images are fused by point product to generate the background suppression map. Secondly, the guided filter is used to further suppress the background and enhance the small target in the map. Thirdly, the small target is detected by the adaptive threshold in the filtered map. The theoretical analyses and experimental results show that the proposed method not only effectively suppresses the clutter background, depending on the edge preserving and denoising characteristics of bilateral filtering, but also effectively highlights the small target, relying on the sensitivity of local entropy to the abrupt gray region. Compared with other methods, it is demonstrated that the proposed method owns lower false alarm rate and higher detection rate.

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Joshi, Deepanjali, and Umesh Joshi. "Analysis of Fast Adaptive Bilateral Filter and Morphological Segmentation on MRI Images." International Journal of Advances in Computer Science and Technology 8, no. 4 (April 15, 2019): 1–6. http://dx.doi.org/10.30534/ijacst/2019/01842019.

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Kaur, Manjeet, Shailender Gupta, and Bharat Bhushan. "An Improved Adaptive Bilateral Filter to Remove Gaussian Noise from Color Images." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 3 (March 31, 2015): 49–64. http://dx.doi.org/10.14257/ijsip.2015.8.3.05.

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Rossi, Alessandro. "Bilateral filter-based adaptive nonuniformity correction for infrared focal-plane array systems." Optical Engineering 49, no. 5 (May 1, 2010): 057003. http://dx.doi.org/10.1117/1.3425660.

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Esakkirajan, Sankaralingam, Chinna Thambi Vimalraj, Rashad Muhammed, and Ganapathi Subramanian. "Adaptive Wavelet Packet-Based De-speckling of Ultrasound Images with Bilateral Filter." Ultrasound in Medicine & Biology 39, no. 12 (December 2013): 2463–76. http://dx.doi.org/10.1016/j.ultrasmedbio.2013.07.009.

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Veerakumar, Thangaraj, Badri Narayan Subudhi, and Sankaralingam Esakkirajan. "Empirical mode decomposition and adaptive bilateral filter approach for impulse noise removal." Expert Systems with Applications 121 (May 2019): 18–27. http://dx.doi.org/10.1016/j.eswa.2018.12.009.

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Sun, Xin, Ning He, Yu-Qing Zhang, Xue-Yan Zhen, Ke Lu, and Xiu-Ling Zhou. "Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5835020.

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In the process of denoising color images, it is very important to enhance the edge and texture information of the images. Image quality can usually be improved by eliminating noise and enhancing contrast. Based on the adaptive wavelet threshold shrinkage algorithm and considering structural characteristics on the basis of color image denoising, this paper describes a method that further enhances the edge and texture details of the image using guided filtering. The use of guided filtering allows edge details that cannot be discriminated in grayscale images to be preserved. The noisy image is decomposed into low-frequency and high-frequency subbands using discrete wavelets, and the contraction function of threshold shrinkage is selected according to the energy in the vicinity of the wavelet coefficients. Finally, the edge and texture information of the denoised color image are enhanced by guided filtering. When the guiding image is the original noiseless image itself, the guided filter can be used as a smoothing operator for preserving edges, resulting in a better effect than bilateral filtering. The proposed method is compared with the adaptive wavelet threshold shrinkage denoising algorithm and the bilateral filtering algorithm. Experimental results show that the proposed method achieves superior color image denoising compared to these conventional techniques.

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Suganthy, M., S. Lakshmi, and S. Palanivel. "Enhancing the Quality of Underwater Images using Fusion of sequential Filters and Dehazing." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 296. http://dx.doi.org/10.14419/ijet.v7i2.24.12067.

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Effectively analyzing underwater images and identifying any object under the water has become a difficult task. Generally, the factors affecting underwater images are uneven lighting, low contrast, blunt colors, and characteristics of an object based on absorption and scattering of light. The proposed technique involves applying white balancing and contrast enhancement to the original image. The combination of filters namely homomorphic filtering, wavelet denoising, bilateral filter , adaptive filters are used and applied sequentially on the degraded underwater images. The results obtained showed that the proposed algorithm works well in refining the underwater image attributes. Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) are used to evaluate performance of the algorithm.

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LEE, Ju Hwan, Sung Yun PARK, Sung Jae KIM, and Sung Min KIM. "The Study of Phase-Based Optical Flow Technique Using an Adaptive Bilateral Filter." IEICE Transactions on Information and Systems E95-D, no. 2 (2012): 658–67. http://dx.doi.org/10.1587/transinf.e95.d.658.

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Nakabayashi, Susumu, Takashi Chikamatsu, Takao Okamoto, Tatsuro Kaminaga, Norikazu Arai, Shinobu Kumagai, Kenshiro Shiraishi, Takahide Okamoto, Takenori Kobayashi, and Jun’ichi Kotoku. "Denoising Projection Data with a Robust Adaptive Bilateral Filter in Low-Count SPECT." International Journal of Medical Physics, Clinical Engineering and Radiation Oncology 07, no. 03 (2018): 363–75. http://dx.doi.org/10.4236/ijmpcero.2018.73030.

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Sridhar, B., K. V. V. S. Reddy, and A. M. Prasad. "An Unsupervisory Qualitative Image Enhancement using Adaptive Morphological Bilateral Filter for Medical Images." International Journal of Computer Applications 99, no. 13 (August 20, 2014): 31–38. http://dx.doi.org/10.5120/17436-8237.

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Mathew, Reeja. "Reduction of Artifacts in Compressed Videos using Adaptive Fuzzy Filter and Bilateral Filter and Comparison of their Performance." International Journal of Computer Applications 16, no. 7 (February 28, 2011): 43–46. http://dx.doi.org/10.5120/2022-2727.

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Kavitha, P., Prabhu Jayagopal, M. Sandeep Kumar, and Vetri Selvi Mahamuni. "A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification." Computational Intelligence and Neuroscience 2022 (December 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/7453935.

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In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%.

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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.

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Chen, Kesong, and Haochen Zhang. "Design of Synchronization Tracking Adaptive Control for Bilateral Teleoperation System with Time-Varying Delays." Sensors 22, no. 20 (October 14, 2022): 7798. http://dx.doi.org/10.3390/s22207798.

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The performances of position synchronization and force interaction of the teleoperation system provide a safe and efficient way for operators to perform tasks in remote, hazardous environments. In practice, however, communication delays and dynamic uncertainties can impair the performance of position synchronization controls. Under the above factors, it is necessary to study and design appropriate bilateral control methods to achieve stable and effective position synchronization control. In this paper, a new adaptive control architecture based on velocity feedback filter and radial basis function neural network is proposed. In the proposed control scheme, only the position signal is transmitted during the communication process, and the speed feedback filter and compensation method are designed and adopted to avoid the use of acceleration signals. In addition, a new auxiliary variable with a tracking error integral term is used to reduce the steady-state error of position tracking under nonzero external environmental forces. Using the Lyapunov–Krasovskii method, the stability of closed-loop remote operating systems is demonstrated. In the simulation and experiment sections, the algorithm was verified separately and compared with other algorithms. The results of a master–slave robot system verify the tracking performance of our proposed control scheme.

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Et.al, D. Devasena. "Despeckling Algorithms For Removing Noise In Medical Images." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 11, 2021): 87–94. http://dx.doi.org/10.17762/turcomat.v12i6.1271.

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The medical and satellite images are mostly corrupted by a multiplicative granular noise called speckle noise which degrades the quality of the images captured by using medical imaging techniques and also Synthetic Aperture Radar images. It causes difficulties in image interpretation and this is mainly due to back scattered signals from the multiple targets. In medical field, the diagnosis of the tissues, bones and organs takes place by using imaging techniques. By using different imaging techniques, the medical images are captured and used for diagnosis. Different types of filtering techniques are proposed in the literature to remove the speckle noise in medical and satellite images. In this research paper different types of adaptive filters and its modifications are proposed and compared. The filters like modified lee filter, modified Edge Enhanced lee filter, modified fast bilateral filter and Modified Particle Swarm Optimization based despeckling algorithm. The results are verified for both simulated images and real medical images and also for Synthetic Aperture Radar images. The results are compared in terms of both objective and subjective analysis for simulated and real medical images. The simulation is done using MATLAB R2013 and the visual qualities of the images are analyzed for varying noise densities.

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Park, Cheul-Woo, Hyuk-Ju Kwon, and Sung-Hak Lee. "Day and Night Adaptive Visible and NIR Image Fusion using the Joint Bilateral Filter." Journal of Korean Institute of Information Technology 20, no. 6 (June 30, 2022): 101–15. http://dx.doi.org/10.14801/jkiit.2022.20.6.101.

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Journal articles on the topic 'Adaptive bilateral filter'

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