Non-Local Sparse Models for Image Restoration

We propose in this paper to unify two different ap- proaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has proven to be very effective in image reconstruction and classification tasks. On the other hand, explicitly exploiting the self-similarities of natural images has led to the success- ful non-local means approach to image restoration. We pro- pose simultaneous sparse coding as a framework for com- bining these two approaches in a natural manner. This is achieved by jointly decomposing groups of similar signals on subsets of the learned dictionary. Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro, Andrew Zisserman
• Keywords: Computer Vision, ICCV 2009, Image Restoration