Total Variation Regularized Low-Rank Tensor Approximation for Color Image Denoising

Yongyong Chen, Yicong Zhou

Abstract: Existing approaches for low-rank approximation either need a rank prior or ignore the spatial smooth characteristic of a color image. To overcome these drawbacks, we propose a total variation regularized low-rank tensor approximation model for color image denoising. The model integrates the strong low-rank prior into a tensor-SVD framework, and introduces the hyper total variation to model the spatial smooth structure of images. Using the alternating direction method of multipliers, we propose a simple algorithm to solve our model. Extensive results on simulated and real noisy color images demonstrate the better performance of the proposed method against state-of-the-art denoising methods.