-Whether sparseness can be effectively controlled is one of the key elements to measure the merits of the sparse coding algorithm. One-norm is primarily used in the sparse coding algorithm to control its sparseness currently, as well as by sparse approximation to control the sparseness of sparse coding model, but all these methods have led to slow convergence and low efficiency ultimately. In o...

A large number of image super resolution algorithms based on the sparse coding are proposed, and some algorithms realize the multi-frame super resolution. In multi-frame super resolution based on the sparse coding, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registra...

In this paper, we propose a novel multilayer sparse coding network capable of efficiently adapting its own regularization parameters to a given dataset. The network is trained end-to-end with a supervised task-driven learning algorithm via error backpropagation. During training, the network learns both the dictionaries and the regularization parameters of each sparse coding layer so that the re...

A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding. Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in discriminative representations. In this work, we develop a supervised sparse...

The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Object recognition has been effectively performed by aggregating sparse codes of local features in an image at multiple spatial scales. Though sparse coding guarantees a highfidelity representation, it does not exploit the dependence between the local features. By incorpor...