Sparse model of multi-directional signal, reconstruction method of sparse model and dictionary training method of reconstruction method

Abstract

The invention discloses a sparse model of a multi-directional model, wherein the sparse model can ensure remarkable improvement on algorithm complexity and storage space without Kronecker product. The sparse model of the multi-directional model is represented by a formula x=B*1D1*2*D2...NDN, wherein ||B||<=K. In the formula, tension x represents a tension product between an N-directional sparse tension B which belongs to R<M1*M2*...*MN and a series of sparse dictionary Dn which belongs to R<In*Mn>, wherein In<=Mn, Dn is defined as the dictionary in an n-th direction, K is sparsity and is used for representing the number of non-zero elements in a sparse coefficient B. The invention furthermore provides a reconstruction method of the sparse model and a dictionary training method of the reconstruction method.

Description

technical field [0001] The invention belongs to the technical field of sparse reconstruction of sparse representation and dictionary training, and in particular relates to a sparse model of multidimensional signals, a reconstruction method and a dictionary training method. Background technique [0002] As an effective method for image and video modeling in recent years, sparse representation has been successfully applied in computer vision fields such as image denoising, super-resolution reconstruction, and face recognition. Traditional sparse representations usually convert multidimensional signals into one-dimensional signals that can be represented by a linear combination of several primitives in a dictionary. High-order signals (images, videos, etc.) first need to be converted into one-dimensional signals, and processed by some methods of vector processing. Research on traditional sparse representation models includes model building, sparse reconstruction, and dictionar...

AI Technical Summary

Problems solved by technology

However, these tensor-based methods decompose or approximate the tensor signal itself. At present, there is no unified framework to describe the tensor sparse representation model, and there are contradictions in algorithm complexity and storage space.

Images