Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation
This paper studies the processing of digital media images using a diffusion equation to increase the contrast of the image by stretching or extending the distribution of luminance data of the image to obtain clearer information of digital media images. In this paper, the image enhancement algorithm...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2022/1982555 |
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| author | Jijun Wang Yi Yuan Guoxiang Li |
| author_facet | Jijun Wang Yi Yuan Guoxiang Li |
| author_sort | Jijun Wang |
| collection | DOAJ |
| description | This paper studies the processing of digital media images using a diffusion equation to increase the contrast of the image by stretching or extending the distribution of luminance data of the image to obtain clearer information of digital media images. In this paper, the image enhancement algorithm of nonlinear diffusion filtering is used to add a velocity term to the diffusion function using a coupled denoising model, which makes the diffusion of the original model smooth, and the interferogram is solved numerically with the help of numerical simulation to verify the denoising processing effect before and after the model correction. To meet the real-time applications in the field of video surveillance, this paper focuses on the optimization of the algorithm program, including software pipeline optimization, operation unit balancing, single instruction multiple data optimization, arithmetic operation optimization, and onchip storage optimization. These optimizations enable the nonlinear diffusion filter-based image enhancement algorithm to achieve high processing efficiency on the C674xDSP, with a processing speed of 25 posts per second for 640×480 size video images. Finally, the significance means a value of super pixel blocks is calculated in superpixel units, and the image is segmented into objects and backgrounds by combining with the Otsu threshold segmentation algorithm to mention the image. In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. By comparing the experimental results qualitatively and quantitatively, it is shown that the algorithm in this paper has an obvious practical effect on contrast enhancement of digital media images and has certain practicality and superiority. |
| format | Article |
| id | doaj-art-429d831316054319a59ba6b5f4ddc1f9 |
| institution | Kabale University |
| issn | 1687-9139 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Mathematical Physics |
| spelling | doaj-art-429d831316054319a59ba6b5f4ddc1f92025-02-03T01:30:39ZengWileyAdvances in Mathematical Physics1687-91392022-01-01202210.1155/2022/1982555Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion EquationJijun Wang0Yi Yuan1Guoxiang Li2Faculty of Information and StatisticsFaculty of College of Computer ScienceFaculty of Information and StatisticsThis paper studies the processing of digital media images using a diffusion equation to increase the contrast of the image by stretching or extending the distribution of luminance data of the image to obtain clearer information of digital media images. In this paper, the image enhancement algorithm of nonlinear diffusion filtering is used to add a velocity term to the diffusion function using a coupled denoising model, which makes the diffusion of the original model smooth, and the interferogram is solved numerically with the help of numerical simulation to verify the denoising processing effect before and after the model correction. To meet the real-time applications in the field of video surveillance, this paper focuses on the optimization of the algorithm program, including software pipeline optimization, operation unit balancing, single instruction multiple data optimization, arithmetic operation optimization, and onchip storage optimization. These optimizations enable the nonlinear diffusion filter-based image enhancement algorithm to achieve high processing efficiency on the C674xDSP, with a processing speed of 25 posts per second for 640×480 size video images. Finally, the significance means a value of super pixel blocks is calculated in superpixel units, and the image is segmented into objects and backgrounds by combining with the Otsu threshold segmentation algorithm to mention the image. In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. By comparing the experimental results qualitatively and quantitatively, it is shown that the algorithm in this paper has an obvious practical effect on contrast enhancement of digital media images and has certain practicality and superiority.http://dx.doi.org/10.1155/2022/1982555 |
| spellingShingle | Jijun Wang Yi Yuan Guoxiang Li Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation Advances in Mathematical Physics |
| title | Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation |
| title_full | Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation |
| title_fullStr | Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation |
| title_full_unstemmed | Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation |
| title_short | Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation |
| title_sort | multifeature contrast enhancement algorithm for digital media images based on the diffusion equation |
| url | http://dx.doi.org/10.1155/2022/1982555 |
| work_keys_str_mv | AT jijunwang multifeaturecontrastenhancementalgorithmfordigitalmediaimagesbasedonthediffusionequation AT yiyuan multifeaturecontrastenhancementalgorithmfordigitalmediaimagesbasedonthediffusionequation AT guoxiangli multifeaturecontrastenhancementalgorithmfordigitalmediaimagesbasedonthediffusionequation |