Image denoising using multidirectional gradient domain

Xiaobo Zhang

doi:10.1007/s11042-021-11184-5

Return

Image denoising using multidirectional gradient domain

2021-07-12

PRE

Xiaobo Zhang *

DOI：10.1007/s11042-021-11184-5

Original

Original request for help

Save

Abstract

En 中文

This paper presents a new two-step image denoising method termed multidirectional gradient domain image denoising (MGDID). In each step, unlike previous gradient domain designs, the multidirectional gradient domain information is used to represent the noise component so that the more directional image features are extracted. The Gaussian pre-filter is carried out in the square gradient coefficients. The nonlinear remedied factor is adopted to modify the denoising amount. The whole denoising process originates from classical nonlocal means (NLM) and nonlinear diffusion. MGDID takes full advantage of ability of NLM to better process the image with the rich repetitive features and the denoising scheme of relatively simplicity and efficiency of nonlinear diffusion. Experimental results show MGDID is superior to the related gradient domain methods and NLM methods in peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM) and visual performance. For example, for Barbara image with the rich repetitive texture feature, MGDID outperforms classical NLM from 0.33 dB to 1.66 dB in PSNR. Usually, classical NLM wins the local adaptive layered Wiener filer (a state-of-the-art gradient domain method) more than 0.44 dB for Barbara. In addition, MGDID is also very efficient compared to the related methods.

Keywords:

Image denoising

Nonlocal means (NLM)

Nonlinear diffusion

Gradient domain

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.