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Abstract
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.
Highlights
In recent years, with the development of information science and technology, images as one of the key mean to obtain news, express news, and transmit news in human’s daily life, and with its different forms of expression occupies a large part of the information expression, the importance of images is gradually discovered and attached to people; so, the research on image processing is launched [1]
One is selective smoothness; that is, the image can be selectively blurred to protect some of the characteristic areas while smoothing out other characteristic areas; the second is that it is easy to produce in the iterative evolution process of the nonlinear diffusion equation
Categorized as background: The mathematical model constructed by the image processing method based on the partial differential equation, which is usually improved based on the partial differential equation of Eq (4), can generally be divided into linear diffusion and nonlinear diffusion methods, which are mainly determined by the diffusion coefficient [16]
Summary
With the development of information science and technology, images as one of the key mean to obtain news, express news, and transmit news in human’s daily life, and with its different forms of expression occupies a large part of the information expression, the importance of images is gradually discovered and attached to people; so, the research on image processing is launched [1]. In the evolution of image enhancement algorithms, the anisotropic diffusion model image enhancement algorithm based on partial differential equations inherited and optimized the local processing enhancement algorithm, where the traditional Perona-Malik model can improve image contrast, increase image details, and reduce noise by combining with gradient calculation [5]. Since this method smoothes the detail part in the image enhancement process, resulting in more loss of detail information in the image during enhancement, there is a need to add the processing of retaining the detail part for this algorithm. Under the premise of ensuring image quality, try to speed up the iterative calculations in the processing process as much as possible
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