Guest Editorial: Sparse Coding

Abstract

Sparse models have gained a tremendous success during the past two decades in various scientific fields. In statistics and machine learning, the sparsity principle is used to perform model selection—that is, selecting a simple model among a large collection of them. This is interpreted as automatically selecting a few predictors that explain the observed data. In signal processing, sparsity is used for approximating signals as a linear combination of a few dictionary elements, imposing a union-of-subspaces model on the true data. Not surprisingly, similar formulations and algorithms have been developed in both these fields, which are now extremely popular in both disciplines. The image processing and computer vision communities have a dominant part in this trend, and we have seen a growing interest in sparse models and their deployment to applications in these fields. In particular, methods where the dictionary is learned from data have been successfully used for a wide range of computer vision and image processing tasks, such as feature and codebook learning, image restoration, super-resolution, compression, visual tracking, and many others. The goal of this special issue is to present the most recent sparse coding techniques dedicated to computer vision and image processing problems, novel applications of sparse cod-