Open AccessAdaptive iterative global image denoising method based on SVD
Author(s) -
Liu Yepeng,
Li Xuemei,
Guo Qiang,
Zhang Caiming
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0082
Subject(s) - singular value decomposition , noise reduction , artificial intelligence , singular value , noise (video) , mathematics , image restoration , pattern recognition (psychology) , similarity (geometry) , non local means , image (mathematics) , iterative method , computer vision , computer science , image processing , algorithm , image denoising , eigenvalues and eigenvectors , physics , quantum mechanics
Based on the image self‐similarity and singular value decomposition (SVD) techniques, the authors propose an iterative adaptive global denoising method. For the structural differences between image patches, they adaptively determine the size of the search window. In each window, a similar image patch matrix is constructed based on the multi‐scale similarity measure. In order to ensure the speed of the method, the adaptive step size and the number of image patches are introduced, and all image patches are denoised in different iterations. This not only ensures the speed of the method, suppresses residual noise, but also reduces the artefacts caused by the fixed step size and the number of image patches. Therefore, the problem of image denoising is converted to the estimation of low‐rank matrix. New singular values are estimated according to the noise level, and similar image patch matrices without noise are estimated using them and corresponding singular vectors. Experimental results show that compared with the state‐of‐the‐art denoising algorithms, this method has a higher PSNR and FSIM, and has a good visual effect. The new method can be applied to image and video restoration, target recognition and image classification.