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Journal articles on the topic 'Adaptive image processing'

To see the other types of publications on this topic, follow the link: Adaptive image processing.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Debayle, Johan, and Jean-Charles Pinoli. "General Adaptive Neighborhood Image Processing:." Journal of Mathematical Imaging and Vision 25, no. 2 (August 14, 2006): 245–66. http://dx.doi.org/10.1007/s10851-006-7451-8.

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Debayle, Johan, and Jean-Charles Pinoli. "General Adaptive Neighborhood Image Processing." Journal of Mathematical Imaging and Vision 25, no. 2 (August 14, 2006): 267–84. http://dx.doi.org/10.1007/s10851-006-7452-7.

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3

Aleksander, I., and M. J. Dobree Wilson. "Adaptive windows for image processing." IEE Proceedings E Computers and Digital Techniques 132, no. 5 (1985): 233. http://dx.doi.org/10.1049/ip-e.1985.0034.

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4

Zhertunova, T. V., and E. S. Yanakova. "ADAPTIVE ALGORITHM BASED ON NONLOCAL MEANS IN IMAGE PROCESSING." Issues of radio electronics, no. 8 (August 20, 2018): 79–86. http://dx.doi.org/10.21778/2218-5453-2018-8-79-86.

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This article describes the existing problem situation associated with the absence of resource-lights denoising algorithms, capable to produce good-quality output images in the different intensity noise conditions without blurring the boundaries, contours and basic structure. The adaptive algorithm proposed in the article allows to solve this problem due to the developed algorithms of splitting the search region into two sets of similar and points different from the pixel and adapting of the kernel type to the image region, depending on the presence or detection of structural and smooth pixels. The results of the proposed algorithm and the standard method of nonlocal means are compared with the metrics of the peak signal-to-noise ratio and structural similarity. It is found out that the developed adaptive algorithm is surpass by far than the standard method both on numerical results and on the quality of the image processing.

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5

Zhang, Lei, Guiping Zheng, Kai Zhang, Yongfeng Wang, Changming Chen, Liting Zhao, Jiquan Xu, et al. "Study on the Extraction of CT Images with Non-Uniform Illumination for the Microstructure of Asphalt Mixture." Materials 15, no. 20 (October 20, 2022): 7364. http://dx.doi.org/10.3390/ma15207364.

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An adaptive image-processing method for CT images of asphalt mixture is proposed in this paper. Different methods are compared according to the error analysis calculated between the real gradation and 3D reconstruction gradation. As revealed by the test results, the adaptive image-processing method was effective in carrying out different brightness homogenization processes for each image. The Wiener filter with 7 × 7 size filter was able to produce a better noise reduction effect without compromising image sharpness. Among the three methods, the adaptive image-processing method performed best in the accuracy of coarse aggregate recognition, followed by the ring division method and the global threshold segmentation method. The error of the gradation extracted by the adaptive image-processing method was found to be lowest compared with the real gradation. For a variety of engineering applications, the developed method helps to improve the analysis of CT images of asphalt mixtures.

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6

Teuner, A., and B. J. Hosticka. "Adaptive Gabor transformation for image processing." IEEE Transactions on Image Processing 2, no. 1 (1993): 112–17. http://dx.doi.org/10.1109/83.210872.

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7

McLean, G. F., and M. E. Jernigan. "Indicator functions for adaptive image processing." Journal of the Optical Society of America A 8, no. 1 (January 1, 1991): 141. http://dx.doi.org/10.1364/josaa.8.000141.

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8

Papanikolaou, V., K. N. Plataniotis, and A. N. Venetsanopoulos. "Adaptive filters for color image processing." Mathematical Problems in Engineering 4, no. 6 (1999): 529–38. http://dx.doi.org/10.1155/s1024123x98000957.

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The color filters that are used to attenuate noise are usually optimized to perform extremely well when dealing with certain noise distributions. Unfortunately it is often the case that the noise corrupting the image is not known. It is thus beneficial to knowa priorithe type of noise corrupting the image in order to select the optimal filter. A method of extracting and characterizing the noise within a digital color image using the generalized Gaussian probability density function (pdf) (B.D. Jeffs and W.H. Pun,IEEE Transactions on Image Processing,4(10), 1451–1456, 1995 andProceedings of the Int. Conference on Image Processing,465–468, 1996), is presented. In this paper simulation results are included to demonstrate the effectiveness of the proposed methodology.

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Richter, G. M., P. Böhm, H. Lorenz, A. Priebe, and M. Capaccioli. "Adaptive filtering in astronomical image processing." Astronomische Nachrichten: A Journal on all Fields of Astronomy 312, no. 6 (1991): 345–49. http://dx.doi.org/10.1002/asna.2113120602.

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Maria Riasat. "Research on various image processing techniques." Open Access Research Journal of Chemistry and Pharmacy 1, no. 1 (December 30, 2021): 005–12. http://dx.doi.org/10.53022/oarjcp.2021.1.1.0029.

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Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that can perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms and gives an image as an output. The most common example is Adobe Photoshop. It is one of the widely used applications for processing digital images. The image processing techniques play a vital role in image Acquisition, image pre-processing, Clustering, Segmentation, and Classification techniques with different kinds of images such as Fruits, Medical, Vehicle, and Digital text images, etc. In this study, the various images remove unwanted noise and performance enhancement techniques such as contrast limited adaptive histogram equalization.

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11

Chuah, Cheng-Soon, and Jin-Jang Leou. "An adaptive image interpolation algorithm for image/video processing." Pattern Recognition 34, no. 12 (December 2001): 2383–93. http://dx.doi.org/10.1016/s0031-3203(00)00157-6.

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12

Dong, Jingming, Iuri Frosio, and Jan Kautz. "Learning Adaptive Parameter Tuning for Image Processing." Electronic Imaging 2018, no. 13 (January 28, 2018): 196–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.13.ipas-196.

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13

Nikolov, Ivan D., and Valentin K. Monovsky. "Adaptive methods and system for image processing." Applied Optics 33, no. 17 (June 10, 1994): 3695. http://dx.doi.org/10.1364/ao.33.003695.

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14

Chen, Dongdong, Qingnan Fan, Jing Liao, Angelica Aviles-Rivero, Lu Yuan, Nenghai Yu, and Gang Hua. "Controllable Image Processing via Adaptive FilterBank Pyramid." IEEE Transactions on Image Processing 29 (2020): 8043–54. http://dx.doi.org/10.1109/tip.2020.3009844.

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15

Cheng, F., and A. N. Venetsanopoulos. "An adaptive morphological filter for image processing." IEEE Transactions on Image Processing 1, no. 4 (1992): 533–39. http://dx.doi.org/10.1109/83.199924.

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16

Chan, P., and J. Lim. "One-dimensional processing for adaptive image restoration." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 1 (February 1985): 117–26. http://dx.doi.org/10.1109/tassp.1985.1164534.

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Plataniotis, K. N., D. Androutsos, and A. N. Venetsanopoulos. "Fuzzy adaptive filters for multichannel image processing." Signal Processing 55, no. 1 (November 1996): 93–106. http://dx.doi.org/10.1016/s0165-1684(96)00122-3.

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18

Buemi, María Elena, Julio Jacobo, and Marta Mejail. "SAR image processing using adaptive stack filter." Pattern Recognition Letters 31, no. 4 (March 2010): 307–14. http://dx.doi.org/10.1016/j.patrec.2009.02.008.

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19

Plataniotis, K. N., D. Androutsos, and A. N. Venetsanopoulos. "Adaptive multichannel filters for colour image processing." Signal Processing: Image Communication 11, no. 3 (January 1998): 171–77. http://dx.doi.org/10.1016/s0923-5965(97)00047-7.

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20

Hu, Min, and Jieqing Tan. "Adaptive osculatory rational interpolation for image processing." Journal of Computational and Applied Mathematics 195, no. 1-2 (October 2006): 46–53. http://dx.doi.org/10.1016/j.cam.2005.07.011.

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21

Plataniotis, K. N., D. Androutsos, S. Vinayagamoorthy, and A. N. Venetsanopoulos. "Color image processing using adaptive multichannel filters." IEEE Transactions on Image Processing 6, no. 7 (July 1997): 933–49. http://dx.doi.org/10.1109/83.597269.

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22

Martens, Jean-Bernard. "Adaptive contrast enhancement through residue-image processing." Signal Processing 44, no. 1 (June 1995): 1–18. http://dx.doi.org/10.1016/0165-1684(95)00011-2.

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23

Li, Jiamu, Wenbo Yu, Yi Wang, Zijian Wang, Jiarong Xiao, Zhongjun Yu, and Desheng Zhang. "Guidance-Aided Triple-Adaptive Frost Filter for Speckle Suppression in the Synthetic Aperture Radar Image." Remote Sensing 15, no. 3 (January 17, 2023): 551. http://dx.doi.org/10.3390/rs15030551.

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Speckle noise exists inherently in the synthetic aperture radar (SAR) image. Its multiplicative property leads to lots of difficulties in SAR image processing. A novel guidance-aided triple-adaptive Frost filter is proposed in this paper, which has potential for real-time processing platforms. Firstly, a scale-adaptive sliding window sizing method is adopted to determine the neighborhood ranges for every point in the image. All the subsequent processing is based on it. Then, an adaptive calculation for the tuning factor in the Frost filter is embedded into the proposed method. Lastly, the feature information apertured from the original image is used to provide guidance for edge recovery automatically, which guarantees the satisfactory ability for feature preservation. Thus, a novel improved Frost filter is proposed with triple adaptabilities. Both the positioning accuracy and response sensitivity of the scale-adaptive sliding window sizing method are verified first. The superiority of the adaptive tuning factor combined with the scale-adaptive sliding window is confirmed by two comparison experiments. At last, the results of speckle suppression experiments on the synthetic images and two natural airborne SAR images present a better performance than other methods.

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24

Wang, Jinjuan, Shan Duan, and Qun Zhou. "An Adaptive Weighted Image Denoising Method Based on Morphology." International Journal of Circuits, Systems and Signal Processing 15 (April 8, 2021): 271–79. http://dx.doi.org/10.46300/9106.2021.15.31.

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In its generation, transmission and record, image signal is often interfered by various noises, which have severally affected the visual effects of images; therefore, it is a very important pre-processing step to take proper approaches to reduce noises. Conventional denoising methods have also blurred image edge information while removing noises, which can be overcome by the method based on mathematical morphology. While eliminating different noises from images, it can not only keep clear object edges, but also preserve as many image details as possible and it also has excellent capacities in noise resistance and edge preservation. With image denoising and mathematical morphology as the research subject, this paper analyzes the generation and characteristics of common image noises, studies the basic theories of mathematical morphology and its applications in image processing, discusses the method to select structural elements in mathematical morphology and proposes a filtering algorithm which combines image denoising and mathematical morphology. This method conducts morphological filtering and denoising on noised image with filter cascade and its performance is verified with stimulation testing. The experiment results prove that the approach to build the morphological filter into cascaded filter through series and parallel connection can to a certain extent, affect the effect of common filter while being applied to different image processing.

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25

A A, Mariena, and J. G.R Sathiaseelan. "Contrast Enhancement of Grayscale and Color images using Adaptive Techniques." International Journal of Engineering & Technology 7, no. 2.22 (April 20, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i2.22.11798.

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Contrast enhancement is an emerging research area in digital image processing domain. It is an important factor in any subjective evaluation of image quality in medical image processing. As there are possibilities for degradation of image quality during the acquisition, there arises the need of an efficient contrast enhancement technique that can remove the redundant pixels from the images prior to final processing. In this paper, we have proposed two adaptive approaches for contrast enhancement. The first approach is used for enhancing grayscale image using mathematical morphology and second approach is for color image using enhanced sigmoid function. The enhancement process of grayscale image was evaluated by using PSNR and that of color image was evaluated by using a factor called measure of contrast. The experimental results indicate that the two proposed methods show better performance for image in grayscale as well as in color.

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26

Prasada Kumari, K. S. "Self-adaptive image processing using blind image quality assessment technique." Perspectives in Science 8 (September 2016): 639–41. http://dx.doi.org/10.1016/j.pisc.2016.06.043.

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27

Jayasri, S. K., and V. Poongodi. "Adaptive Neuro-Fuzzy Inference System Based Impulse Denoising." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1847–51. http://dx.doi.org/10.1166/jctn.2020.8452.

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Removing impulse noise from images is a critical issue in image processing because it may occur frequently during acquisition or transmission of images. We propose an anfis based impulse denoising algorithm to preserve the intrinsic geometric details of an image. The main target of this project is to restore the features of an image without losing any information from the degraded image. This method is more suitable to preserve the features of an image with scale invariant properties of an image. Here we are performing the training the noisy image with ANFIS and testing the image to retain the features of an original image.

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28

Chen, Mujun. "Automatic Image Processing Algorithm for Light Environment Optimization Based on Multimodal Neural Network Model." Computational Intelligence and Neuroscience 2022 (June 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/5156532.

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In this paper, we conduct an in-depth study and analysis of the automatic image processing algorithm based on a multimodal Recurrent Neural Network (m-RNN) for light environment optimization. By analyzing the structure of m-RNN and combining the current research frontiers of image processing and natural language processing, we find out the problem of the ineffectiveness of m-RNN for some image generation descriptions, starting from both the image feature extraction part and text sequence data processing. Unlike traditional image automatic processing algorithms, this algorithm does not need to add complex rules manually. Still, it evaluates and filters through the training image collection and finally generates image automatic processing models by m-RNN. An image semantic segmentation algorithm is proposed based on multimodal attention and adaptive feature fusion. The main idea of the algorithm is to combine adaptive and feature fusion and then introduce data enhancement for small-scale multimodal light environment datasets by extracting the importance between images through multimodal attention. The model proposed in this paper can span the semantic differences of different modalities and construct feature relationships between different modalities to achieve an inferable, interpretable, and scalable feature representation of multimodal data. The automatic processing of light environment images using multimodal neural networks based on traditional algorithms eliminates manual processing and greatly reduces the time and effort of image processing.

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29

Yan, Xuehu, Lei Sun, Yuliang Lu, and Guozheng Yang. "Adaptive Partial Image Secret Sharing." Symmetry 12, no. 5 (May 2, 2020): 703. http://dx.doi.org/10.3390/sym12050703.

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In contrast to encrypting the full secret image in classic image secret sharing (ISS), partial image secret sharing (PISS) only encrypts part of the secret image due to the situation that, in general, only part of the secret image is sensitive or secretive. However, the target part needs to be selected manually in traditional PISS, which is human-exhausted and not suitable for batch processing. In this paper, we introduce an adaptive PISS (APISS) scheme based on salience detection, linear congruence, and image inpainting. First, the salient part is automatically and adaptively detected as the secret target part. Then, the target part is encrypted into n meaningful shares by using linear congruence in the processing of inpainting the target part. The target part is decrypted progressively by only addition operation when more shares are collected. It is losslessly decrypted when all the n shares are collected. Experiments are performed to verify the efficiency of the introduced scheme.

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Likhith, H. G., H. L. Nishanth, E. Lohit, M. S. Rudramurthy, S. A. Sushma, and T. G. Keerthan Kumar. "Development of data driven adaptive edge detectors for image processing." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (September 1, 2021): 012032. http://dx.doi.org/10.1088/1757-899x/1187/1/012032.

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Abstract In most vision processing activities, the early stage involves identifying the features in an image that provide cues to structure and properties of the object in the scene. Most common features in an image or in a scene arel edges. Edges arel significant local changes in intensity within anlimage. Most important goal of edge detection is to produce a line drawing from anlimage representing the scene. The significant features of an image such as line, curve and corners can be extracted from edges. During the stage of discovering and exploring the information from an image of that scene, edge detection is the most important and early-stage activity and as such it is prominent active area in image processing. Most popular edge detection algorithm such as Robert, Sobel, Canny, Prewitt and Laplacian of Gaussian (LoG), etc. are currently in use. This paper emphasis on an experimental study of limitations of conventional edge detectors and to devise a novel approach to resolve the conflicting issues i.e., limitations of these edge detectors in adaptive space utilizing novel methods such as Bi-dimensional Empirical Mode Decomposition (BEMD), Image Empirical Mode Decomposition (IEMD), Complete Ensemble Empirical Mode Decomposition (CEEMD) and Multivariate Decomposition techniques. Further, to study the performance of these modified edge detectors on the images of complex scenes which are of societal and agricultural importance.

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31

Erler, K., and E. Jernigan. "Adaptive image restoration using recursive image filters." IEEE Transactions on Signal Processing 42, no. 7 (July 1994): 1877–81. http://dx.doi.org/10.1109/78.298306.

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32

Sun, Yinlong, Bartek Rajwa, and J. Paul Robinson. "Adaptive image-processing technique and effective visualization of confocal microscopy images." Microscopy Research and Technique 64, no. 2 (2004): 156–63. http://dx.doi.org/10.1002/jemt.20064.

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33

Peng, Na Xin, and Yu Qiang Chen. "Improved Self-Adaptive Image Histogram Equalization Algorithm." Advanced Materials Research 760-762 (September 2013): 1495–500. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1495.

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Histogram equalization (HE) algorithm is wildly used method in image processing of contrast adjustment using images histogram. This method is useful in images with backgrounds and foreground that are both bright or both dark. But the performance of HE is not satisfactory to images with backgrounds and foregrounds that are both bright or both dark. To deal with the above problem, [ gives an improved histogram equalization algorithm named self-adaptive image histogram equalization (SIHE) algorithm. Its main idea is to extend the gray level of the image which firstly be processed by the classical histogram equalization algorithm. This paper gives detailed introduction to SIHE and analyzes the shortage of it, then give an improved version of SIHE named ISIHE, finally do experiments to show the performance of our algorithm.

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Lee, Hosang. "Successive Low-Light Image Enhancement Using an Image-Adaptive Mask." Symmetry 14, no. 6 (June 6, 2022): 1165. http://dx.doi.org/10.3390/sym14061165.

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Low-light images are obtained in dark environments or in environments where there is insufficient light. Because of this, low-light images have low intensity values and dimmed features, making it difficult to directly apply computer vision or image recognition software to them. Therefore, to use computer vision processing on low-light images, an image improvement procedure is needed. There have been many studies on how to enhance low-light images. However, some of the existing methods create artifact and distortion effects in the resulting images. To improve low-light images, their contrast should be stretched naturally according to their features. This paper proposes the use of a low-light image enhancement method utilizing an image-adaptive mask that is composed of an image-adaptive ellipse. As a result, the low-light regions of the image are stretched and the bright regions are enhanced in a way that appears natural by an image-adaptive mask. Moreover, images that have been enhanced using the proposed method are color balanced, as this method has a color compensation effect due to the use of an image-adaptive mask. As a result, the improved image can better reflect the image’s subject, such as a sunset, and appears natural. However, when low-light images are stretched, the noise elements are also enhanced, causing part of the enhanced image to look dim and hazy. To tackle this issue, this paper proposes the use of guided image filtering based on using triple terms for the image-adaptive value. Images enhanced by the proposed method look natural and are objectively superior to those enhanced via other state-of-the-art methods.

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35

WANG, ZHIYONG, ZHERU CHI, DAGAN FENG, and AH CHUNG TSOI. "CONTENT-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK USING ADAPTIVE PROCESSING OF TREE-STRUCTURE IMAGE REPRESENTATION." International Journal of Image and Graphics 03, no. 01 (January 2003): 119–43. http://dx.doi.org/10.1142/s0219467803000944.

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Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.

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36

Amirany, Abdolah, Gavin Epperson, Ahmad Patooghy, and Ramin Rajaei. "Accuracy-Adaptive Spintronic Adder for Image Processing Applications." IEEE Transactions on Magnetics 57, no. 6 (June 2021): 1–10. http://dx.doi.org/10.1109/tmag.2021.3069161.

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F.R.daSilva, José, Ruy A. C. Altafim, and André R. Hirakawa. "Cross-arms Identification with Adaptive Digital Image Processing." International Journal of Computer Applications 121, no. 23 (July 18, 2015): 36–39. http://dx.doi.org/10.5120/21843-5118.

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Murino, Vittorio, Gian Luca Foresti, and Carlo S. Regazzoni. "A Belief-Based Approach for Adaptive Image Processing." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 03 (May 1997): 359–92. http://dx.doi.org/10.1142/s0218001497000160.

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This paper proposes a new approach to the problem of intelligently regulating image-processing parameters of a distributed network. The proposed approach is based on two-step probabilistic process: (a) belief updating, which consists in computing a functional cost at each node of the network and, (b) belief maximization, which depends on maximizing this functional cost by using a stochastic optimization algorithm. The architecture of an image processing system, consisting of three modules connected in a chain-like structure, is presented as an example showing the capabilities of the proposed approach. Each module is provided with a priori information about the set of parameters that manage a particular data transformation, and with evaluation criteria to judge data quality and to decide on the parameters to be adjusted. Experimental results obtained by using a digitally controlled camera and lens objective, are presented to show the validity of the proposed approach.

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Plataniotis, K. N., D. Androutsos, and A. N. Venetsanopoulos. "An adaptive multichannel filter for colour image processing." Canadian Journal of Electrical and Computer Engineering 21, no. 4 (October 1996): 149–52. http://dx.doi.org/10.1109/cjece.1996.7101993.

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Plataniotis, K. N., D. Androutsos, and A. N. Venetsanopoulos. "Color image processing using adaptive vector directional filters." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 45, no. 10 (1998): 1414–19. http://dx.doi.org/10.1109/82.728854.

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Lin Yia, J. T. Astola, and Y. A. Neuvo. "Adaptive Stack Filtering with Application to Image Processing." IEEE Transactions on Signal Processing 41, no. 1 (January 1993): 162. http://dx.doi.org/10.1109/tsp.1993.193136.

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42

Fryzlewicz, Piotr, and Catherine Timmermans. "SHAH: SHape-Adaptive Haar Wavelets for Image Processing." Journal of Computational and Graphical Statistics 25, no. 3 (July 2, 2016): 879–98. http://dx.doi.org/10.1080/10618600.2015.1048345.

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Amat, Sergio, Rosa Donat, Jacques Liandrat, and J. Carlos Trillo. "A fully adaptive multiresolution scheme for image processing." Mathematical and Computer Modelling 46, no. 1-2 (July 2007): 2–11. http://dx.doi.org/10.1016/j.mcm.2006.12.003.

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44

Veelaert, Peter, and Kristof Teelen. "Adaptive and optimal difference operators in image processing." Pattern Recognition 42, no. 10 (October 2009): 2317–26. http://dx.doi.org/10.1016/j.patcog.2008.11.017.

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Chen, Xiaofang, Weihua Gui, Chunhua Yang, Kaijun Zhou, and Hong Wang. "Adaptive Image Processing for Bubbles in Flotation Process." Measurement and Control 44, no. 4 (May 2011): 121–26. http://dx.doi.org/10.1177/002029401104400405.

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46

Caton, Simon J., Omer F. Rana, and Bruce G. Batchelor. "Distributed image processing over an adaptive Campus Grid." Concurrency and Computation: Practice and Experience 21, no. 3 (March 10, 2009): 321–36. http://dx.doi.org/10.1002/cpe.1357.

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47

Chen, Yuansheng, Yuansheng Song, Wendong Chen, Tingzhou Mu, and Yuan Ji. "Visual Perception Adaptive Image Processing for HD Microdisplay." SID Symposium Digest of Technical Papers 51, S1 (July 2020): 31–34. http://dx.doi.org/10.1002/sdtp.13744.

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48

Mironov, S. V., K. N. Dudkin, and A. K. Doudkine. "Separation of Figure from Ground as an Adaptive Image Processing." Perception 26, no. 1_suppl (August 1997): 270. http://dx.doi.org/10.1068/v970139.

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We define the separation of figure from ground as a visual-attribute-dependent and task-dependent representation of sensory information in higher-level visual processes. A computer model for adaptive segmentation of 2-D visual objects (Dudkin et al, 1995 Proceedings of SPIE 122) was developed in these studies. The description and separation of figure from ground are implemented by spatial frequency filters and feature detectors performing as self-organising mechanisms. The simulation of control processes caused by attention (top - down), and lateral, frequency-selective, and cross-orientation inhibition (bottom - up) determines the adaptive image processing. The first stage is the estimation of input image produced by the analysis of the spatial brightness distribution by algorithms calculating the vector of primary descriptive attributes. These results provide the synthesis of control processes based on several algorithms, each of which transforms descriptive attributes into separate control parameters. The creation of two primary descriptions: ‘sustained’ (contours) and ‘transient’ (fragments with homogeneous intensity), and the selection of feature-detection operators are governed by the complete set of control parameters. The primary descriptions allow formation of the intermediate image description in which similar elements are grouped by identical brightness, colour, spatial position, curvature, and texture according to Gestalt concepts. To divide the image into basic areas and to extract fragments which belong to a putative figure, all these descriptions are combined into the final integrated image representation. The model has been tested on various images.

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Chen, Ming. "Fractional-Order Adaptive P -Laplace Equation-Based Art Image Edge Detection." Advances in Mathematical Physics 2021 (August 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/2337712.

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In recent years, with the rapid development of image processing research, the study of nonstandard images has gradually become a research hotspot, for example, fabric images, remote sensing images, and gear images. Some of the remote sensing images have a complex background and low illumination compared with standard images and are easy to be mixed with noise during acquisition; some of the fabric images have rich texture information, which adds difficulty to the related processing, and are also easy to be mixed with noise during acquisition. In this paper, we propose a fractional-order adaptive P -Laplace equation image edge detection algorithm for the problem of image edge detection in which the edge and texture information of the image is lost. The algorithm can apply for the order adaptively to filter the noise according to the noise distribution of the image, and the adaptive diffusion factor is determined by both the fractional-order curvature and fractional-order gradient of the iso-illumination line and combined with the iterative approach to realize the fine-tuning of the noisy image. The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. A fractional-order partial differential equation image edge detection model with a fractional-order fidelity term is proposed for Gaussian noise. The model incorporates a fractional-order fidelity term because this fidelity term smoothes out the rougher parts of the image while preserving the texture in the original image in greater detail and eliminating the step effect produced by other models such as the Perona-Malik (PM) and Rudin-Osher-Fatemi (ROF) models. By comparing with other algorithms, the image edge detection effect is measured with the help of evaluation metrics such as peak signal-to-noise ratio and structural similarity, and the optimal value is selected iteratively so that the image with the best edge detection result is retained. A convolutional mask image edge detection model based on adaptive fractional-order calculus is proposed for the scattered noise in medical images. The adaption is mainly reflected in the model algorithm by constructing an exponential parameter relation that is closely related to the image, which can dynamically adjust the parameter values, thus making the model algorithm more practical. The model achieves the scattering noise removal in four steps.

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Cao, Yue Qin. "A Study on Compression Processing Technology of Static Images." Advanced Materials Research 433-440 (January 2012): 676–80. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.676.

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With the rapid development of computers, communication and multimedia electronic products, application of high quality images is becoming more and more popular. Improvement of image quality is a very important subject at present. Basic on compression technology of static images, this subject raises adaptive quantitative methods for different images, adopts secondary calculation method during quantization, and then gives simulation validation to images by Matlab software. According to the rate of high-frequency and low-frequency of images, adjust quantization table to make the best effort of image compression.

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