Sparse Dictionary Learning for Image Recovery by Iterative Shrinkage
This is an incremental study for researchers in image processing and sparse coding, focusing on comparing existing methods rather than introducing new ones.
The paper tackled the problem of image recovery via sparse dictionary learning by comparing several state-of-the-art sparse optimization methods based on shrinkage operations, finding that the choice of optimization method is practically important depending on training data availability and computational efficiency.
In this paper we study the sparse coding problem in the context of sparse dictionary learning for image recovery. To this end, we consider and compare several state-of-the-art sparse optimization methods constructed using the shrinkage operation. As the mathematical setting of these methods, we consider an online approach as algorithmical basis together with the basis pursuit denoising problem that arises by the convex optimization approach to the dictionary learning problem. By a dedicated construction of datasets and corresponding dictionaries, we study the effect of enlarging the underlying learning database on reconstruction quality making use of several error measures. Our study illuminates that the choice of the optimization method may be practically important in the context of availability of training data. In the context of different settings for training data as may be considered part of our study, we illuminate the computational efficiency of the assessed optimization methods.