基于非局部自相似性的低对比度图像降噪算法

doi:10.13229/j.cnki.jdxbgxb.20240594

Abstract

Abstract:

In order to improve the edges smoothness and clarity of low contrast image， a low contrast image denoising algorithm based on non local self similarity is designed in this paper. Based on the difference between image blocks in low contrast images， a self similarity dataset is constructed. On this basis， the similarity of image blocks is calculated， and a binary identification matrix is used to distinguish noise points and effective pixel， weight coefficients are introduced to perform weighted averaging on the pixels， resulting in a transition image. Therefore， based on the absolute difference sum between the original image and the transition image is calculated to determined whether has completed denoising. If the absolute difference sum is less than or equal to 0， denoising is achieved. The experimental results show that the SSIM of the proposed algorithm has remained around 0.9， and the denoised image is clearer and more realistic. It is shown that the proposed algorithm can better preserve the original information and structural information， making the denoised images more natural and realistic visually.

Key words: non local self similarity, low contrast images, image denoising, transition image, salt and pepper noise

CLC Number:

TP391

Di-ren LIU,Ao MA. Low contrast image denoising algorithm based on non local self similarity[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(7): 2378-2382.

Figures/Tables 5

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Table 1

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References 16

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参数名称	参数值
图像块大小	8×8
差异度阈值	100
搜索窗口大小	32×32
相似度阈值	0.7
二值化阈值	20%
滤波窗口大小	5×5
ADS阈值	1 000

Low contrast image denoising algorithm based on non local self similarity

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