WEIGHTED TENSOR NUCLEAR NORM MINIMIZATION FOR COLOR IMAGE DENOISING

Although non-local image denoising has attracted much research effort due to its superior performance, little attention has focused on its color extension. Most existing non-local color image denoising methods process the color channels of an input image separately. However, in order to improve the performance of color image denoising, all color channels should be processed jointly for fully utilizing the inter channel dependency. This paper proposes a new non-local and inter-channel dependency aware prior, named weighted tensor nuclear norm (WTNN), and it is defined on a 3rd-order tensor from a patch cluster of an input image. We also present an effective algorithm for color image denoising using the WTNN. Experimental results clearly show that the proposed algorithm outperforms a state-of-the-art color image denoising method, known as CBM3D.