Tensor-Based Light Field Denoising by Integrating Super-Resolution

Light field, a promising representation to describe the scene appearance, is susceptible to various noise due to the current sensor design. This paper proposes a novel tensor-based denoising method for the 4D light field that consists of two main steps. First, we generalize the intrinsic tensor sparsity measure to light field images by exploiting the nonlocal similarity across the spatial and angular dimensions. Second, we further exploit the spatial-angular correlation by integrating light field super-resolution into the denoising process to eliminate the sub-pixel misalignment of different views. After a back-projection from the refined high-resolution central view under an intensity consistency criteria, the denoising performance for the light field can be boosted. Experimental results validate the superior performance of the proposed method in terms of both PSNR and visual quality on the HCI light field dataset.