Sparse representation depth image reconstruction algorithm based on structure dictionary

Abstract

The invention discloses a sparse representation depth image reconstruction algorithm based on a structure dictionary, belonging to the image processing technology field. Firstly, the sparse representation depth image reconstruction algorithm of the invention considers a corresponding depth image and a corresponding color image as a whole, and, during the solution process, the sparse representation depth image reconstruction algorithm improves the reconstruction effect of the depth image and the color image through constructing a structure dictionary having a logic corresponding relation. During the dictionary construction process, the sparse representation depth image reconstruction algorithm based on the structure dictionary utilizes the logical corresponding relation between the depth image and the color image and the similarity relation of the depth image to improve the efficiency and quality of dictionary training. The depth image reconstruction algorithm combines the related theories like the sparse coding on the basis of collecting and arranging a lot of home and abroad related data, analyzes the correlation between data according to the close correlation between the depth image and the color image, mainly solves the reconstruction problem of the sparse representation depth image based on the structure dictionary, reduces the operation complexity, and improves the reconstruction quality of the depth image and the corresponding color image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a sparse representation depth image reconstruction algorithm based on a structure dictionary. Background technique [0002] Sparse representation is a signal representation method, that is, a given signal can be reconstructed by a baseline sparse in a transform domain. The collection of bases in the transform domain is called a dictionary. In order to make the representation of the signal invariant to translation, the researchers introduced the concept of redundant representation, that is, the dimension of the representation coefficient of the signal in the transform domain is required to be larger than the dimension of the signal itself, that is, "over-complete". By looking for a set of "super-complete" basis vectors to represent the sample data more efficiently, a signal can be expressed as a linear combination of a set of basis vectors. Under the over-co...

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Problems solved by technology

However, due to the lack of user experience or the limitations of the device itself, the obtained depth images often have problems such as noise and missing depth values.

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