Color image denoising based on low-rank tensor train

Yang Zhang; Zhi Han; Yandong Tang

doi:10.1117/12.2524189

6 May 2019 Color image denoising based on low-rank tensor train

Yang Zhang, Zhi Han, Yandong Tang

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110692P (2019) https://doi.org/10.1117/12.2524189
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

Tensor has been widely used in computer vision due to its ability to maintain spatial structure information. Owning to the well-balanced unfolding matrices, the recently proposed tensor train (TT) decomposition can make full use of information from tensors. Thereby, tensor train representation has a better performance in many fields compared to traditional methods of tensor decomposition. Inspired by the success of tensor train, in this paper, we firstly apply lowrank tensor train to recovering noisy color images. Meanwhile, we propose a novel algorithm for noise-contaminated images based on the block coordinate descent (BCD) method. The numerical experiments demonstrate that our algorithm can give a better result in the real color image both visually and numerically.

Citation Download Citation

Yang Zhang, Zhi Han, and Yandong Tang "Color image denoising based on low-rank tensor train", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692P (6 May 2019); https://doi.org/10.1117/12.2524189

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KEYWORDS

Denoising

Matrices

Computer vision technology

Machine vision

Image denoising

Image processing

RGB color model

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