Modeling signals by sparse and redundant representations has been drawing considerable attention in recent years. Coupled with the ability to train the dictionary using signal exam...
In recent years, sparse representation originating from signal compressed sensing theory has attracted increasing interest in computer vision research community. However, to our b...
In this work, we propose the use of sparse signal representation techniques to solve the problem of closed-loop spatial image prediction. The reconstruction of signal in the block...
The dictionary approach to signal and image processing has been massively investigated in the last two decades, proving very attractive for a wide range of applications. The effec...
Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization pro...