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Two-stage image denoising algorithm based on noise localization
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AI
DOI：10.1007/s11042-020-10428-0.png)
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摘要
En 中文
At present, most denoising algorithms cannot determine whether a pixel is noise, but these use the same rules to process all pixels. Most denoising methods will filter out the original image information when they deal with images with more details or little difference between the subject and the background. In order to improve the above shortcomings, a two-stage image denoising algorithm of noise localization in this paper is proposed.Firstly, the thresholds T-1 ' and T-2 ' are extracted according to the image gray value distribution. Image edge information is removed and saved by edge extraction, this gets an edgeless greyscale image. Secondly, singular value decomposes the edgeless image to obtain the singular value matrix, the percentage threshold eta is used to reduce the singular value matrix.The coarse noise filtering is performed by the inverse matrix decomposition. Again, the adaptive thresholds T-1 and T-2 are calculated with the histogram, the image is divided into Dark Area, Gray Area and Light Area. Then, a superpixel-like algorithm is introduced to determine and remove the noise accurately in three regions. Finally, the image edges are combined with the denoised image. By analyzing the denoising image and comparing the peak signal-to-noise ratio (PSNR) and time of the result in many images, it is verified that the proposed algorithm has a better denoising effect than many other denoising algorithms.
Keyword:
Adaptive threshold
Noise localization
Edge extraction
Image denoising
Singular value decomposes
Superpixel-like algorithm
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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