Similar Patch Selection in Embedding Space for Multi-View Image Denoising

This paper proposes an image patch selection that finds similar patches in multiple images so that image denoising can suppress noise more effectively by exploiting the identified similar patches from the multi-view images. We encode all image patches in multi-view images into a low-dimensional space, and it allows for a denoiser to find similar patches effectively from the space. Our approach enables existing patch-based denoisers, which often find similar patches within an image window, to identify more similar patches by extending the limited search space into the entire space (i.e., all input images). We integrate our technique into state-of-the-art single-view denoising (block-matching and 3D filtering (BM3D)), and demonstrate that the BM3D combined with our approach is able to conduct multi-view image denoising effectively, without a major alteration to the existing algorithm.