Efficient non-local kernel regression with structural classification for multiview image denoising

This paper presents an effective image structure classification method, which was recently proposed for selecting the key parameter of non-local kernel regression (NLKR) namely the kernel bandwidth. Meanwhile, to overcome the problem of intensive computation cost of the non-local patch searching in NLKR, a fast patch searching strategy is proposed according to the classified structure regions. The proposed structure-aware NLKR (SA-NLKR) is applied to the multiview image denoising problem and the effectiveness of the proposed algorithm is illustrated by experimental results.