A Nonlocal Denoising Framework Based on Tensor Robust Principal Component Analysis with ℓp norm

Mengqing Sun, Li Zhao, Jingjing Zheng, Jiawei Xu

Abstract: This paper have given a nonlocal denoising framework based on tensor robust principal component analysis with ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> norm for color image and video (NDFCIV), which have following three features: (1) it is capable of processing zero-mean Gaussian noise, impulse noise and any other noise that is created by mixing the two for color image and video at same time. (2) Meanwhile, nonlocal denoising strategy is adopted to promote the effectiveness of the denoising framework. (3) Moreover, we present a non-convex constraint method which can get more exact low rank tensor recovery result and enhance the denoising effect of the framework further. The experimental results demonstrate the effectiveness of the proposed denoising framework.