An Image Deniosing Algorithm Based on Fast Non-local Mean and Super-resolution Reconstruction

doi:10.3969/j.issn.1000-1093.2021.08.016

Abstract

Abstract: Most of the existing image denoising algorithms can only deal with the noise intensity varying in a limited range. For the actual image noise intensity varying in a wide range, an image denoising algorithm is proposed based on the fast non-local means algorithm and the deep residual convolutional network-based super-resolution reconstruction algorithm. The improved non-local means algorithm and Box filter are used to denoise the image initially, and then the initial denoised image is reconstructed with end-to-end super-resolution of low-resolution images by the deep residual convolutional network. The simulated results show that the proposed algorithm can be used to obtain higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared with other classical denoising algorithms when the noise intensities are 15, 25, 40, 50 and 60. And with the increase in noise intensity, its advantage is more and more obvious. The proposed algorithm is suitable for denoising of known noise and blind noise. And the proposed algorithm is superior to the classical noise reduction algorithms in blind noise reduction. In addition, the proposed algorithm can restore the image details better and generate a better visual effect.

Key words: imagedeniosing, non-localmean, Boxfilter, deepresidualconvolutionalnetwork, super-resolution

CLC Number:

TP751.1

LI Jing, LIU Zhe, HUANG Wenzhun. An Image Deniosing Algorithm Based on Fast Non-local Mean and Super-resolution Reconstruction[J]. Acta Armamentarii, 2021, 42(8): 1716-1727.

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