Adaptive iterative global image denoising method based on SVD. Issue 13 (5th October 2020)

• Record Type: Journal Article
• Authors: Liu, Yepeng; Li, Xuemei; Guo, Qiang; Zhang, Caiming
• Is Part Of: IET image processing. Volume 14:Issue 13(2020)
• Page Start: 3028
• Page End: 3038
• Publication Date: 2020-10-05
• Subjects: image denoising -- iterative methods -- image classification -- singular value decomposition -- matrix algebra -- video signal processing -- image restoration; SVD -- image self‐similarity -- singular value decomposition -- similar image patch matrix -- multiscale similarity measure -- video restoration -- target recognition -- image classification -- adaptive iterative global image denoising -- PSNR -- FSIM -- image restoration; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0082
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.