Image Sparse Representation Research And Application Based On The Study Of Multi-dictionaries

The overcomplete sparse representation can represent images in a compact andefficient way, and only few nonzero coefficients can reveal the intrinsic structures andessential properties of images. Sparse representation model can effectively match thesparse coding strategy in the primary visual cortex of human. Aiming to the image sparserepresentation research based on the study of multi-dictionaries, the sparse representationmotivated by processing mechanism of human brain are performed. The maincontributions of this dissertation are as follows:Firstly, a layered dictionary learning method based on the union spaces is proposed,inspired by the fact that the simple cell receptive fields in V1area are only sensitive tothe strip stimulation with a certain direction. The whole image space is composed ofunion of subspaces with different structures. In every subspace, a layered dictionary isconstructed, which can match the residual’s structures in the iteration process oforthogonal matching pursuit.Secondly，based on the hierarchical propertie of human visual perception system，aeffective algorithm based on the combination of atom dictionary and molecule dictionaryis proposed aiming to the problem that current sparse representation lacks the propertiesof the complex cell receptive fields in V2area. We construct the atom space withabundant primary visual features and molecule space with abundant intermediate visualfeatures, and then we learn the optimal dictionary in every subspace.Finally, aiming to the problem that the data can’t be recovered effectively using onlysparse coding when the observations are highly reduced in dimensions and corruptedwith high noise, a efficient sparse representation algorithm is proposed. This ideacombines two priors: the manifold projection and the sparse coding. The algorithmassumes that the samples in the same submanifold have similar structures, and this ideaensures data recovered using sparse approximation is closed to its manifold. Experimentresults show the validity of the algorithm.