Classical image denoising algorithms based on single noisy images and generic image databases will soon reach their performance limits. In this paper, we propose to denoise images using targeted external image databases. Formulating denoising as an optimal filter design problem, we utilize the targeted databases to (1) determine the basis functions of the optimal filter by means of group sparsity; (2) determine the spectral coefficients of the optimal filter by means of localized priors. For a variety of scenarios such as text images, multiview images, and face images, we demonstrate superior denoising results over existing algorithms.
Enming Luo, Stanley H. Chan, and Truong Q. Nguyen, “Image Denoising by Targeted External Databases”, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2014. (Awarded ICASSP Student Travel Grant)