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Introducing a New Hybrid Adaptive Local Optimal Low Rank Approximation Method for Denoising Images | ||
| 𝕴𝖓𝖙𝖊𝖗𝖓𝖆𝖙𝖎𝖔𝖓𝖆𝖑 𝕵𝖔𝖚𝖗𝖓𝖆𝖑 𝖔𝖋 𝕴𝖓𝖉𝖚𝖘𝖙𝖗𝖎𝖆𝖑 𝕰𝖑𝖊𝖈𝖙𝖗𝖔𝖓𝖎𝖈𝖘, 𝕮𝖔𝖓𝖙𝖗𝖔𝖑 𝖆𝖓𝖉 𝕺𝖕𝖙𝖎𝖒𝖎𝖟𝖆𝖙𝖎𝖔𝖓 | ||
| مقاله 7، دوره 3، شماره 2، تابستان 2020، صفحه 173-185 اصل مقاله (1.05 MB) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2019.31245.1199 | ||
| نویسندگان | ||
sadegh kalantari   ؛ mehdi ramezani؛ ali madadi
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| Department of Electrical Engineering,Control Group,Tafresh University | ||
| چکیده | ||
| This paper aimed to formulate image noise reduction as an optimization problem and denoise the target image using matrix low rank approximation. Considering the fact that the smaller pieces of an image are more similar (more dependent) in natural images; therefore, it is more logical to use low rank approximation on smaller pieces of the image. In the proposed method, the image corrupted with AWGN (Additive White Gaussian Noise) is locally denoised, and the optimization problem of low rank approximation is solved on all fixed-size patches (Windows with pixels needing to be processed). This method can be implemented in parallelly for practical purposes, because it can simultaneously handle different image patches. This is one of the advantages of this method. In all noise reduction methods, the two factors, namely the amount of the noise removed from the image and the preservation of the edges (vital details), are very important. In the proposed method, all the new ideas including the use of TI image (Training Image) and SVD adaptive basis, iterability of the algorithm and patch labeling have all been proved efficient in producing sharper images, good edge preservation and acceptable speed compared to the state-of-the-art denoising methods. | ||
| کلیدواژه‌ها | ||
| Optimal Low Rank Approximation؛ SVD؛ Signal Denoising؛ Image Denoising؛ Signal Processing | ||
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آمار تعداد مشاهده مقاله: 225 تعداد دریافت فایل اصل مقاله: 141 |
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