Image video scene content conformal mapping sparse representation method

Abstract

The invention provides an image video scene content conformal mapping sparse representation method. The method comprises the following steps: 1) inputting an original image or video and carrying out sampling in a characteristic space; 2) calculating K nearest neighbor of each sample and establishing a local full adjacent map and calculating the distance between adjacent samples; 3) according to conformal mapping rules, combining the conformal mapping rules with the sparse representation method and learning a dictionary having a conformal property; and 4) carrying out reconstruction on the original image or video by utilizing the dictionary. According to the method, by introducing the conformal mapping rules, angle information between the adjacent samples is kept to the maximum degree, and the dictionary having more powerful presentation capability is obtained. Meanwhile, conformal mapping drives the adjacent samples to use the similar dictionary to carry out reconstruction, so that the dictionary is allowed to be more concise and compact. The method has a wide application prospect in the technical field of picture processing, computer vision and reality augmentation.

Description

technical field [0001] The invention relates to the technical fields of image processing, computer vision and augmented reality, in particular to a conformal mapping sparse expression method for image and video scene content. Background technique [0002] In recent years, sparse representation and dictionary learning techniques have received a lot of attention as a research hotspot, and have been widely used in image processing and computer vision, such as image super-resolution, image denoising, classification and color editing, etc. The sparse representation technique is to reconstruct the signal by linear combination of samples in the over-complete dictionary, and limit the number of reconstructed samples to achieve the sparse property. [0003] At present, many researchers are devoted to the study of sparse representation methods, and dictionaries play a very important role in sparse representation techniques. Michal Aharon et al. proposed the K-SVD dictionary learning ...

AI Technical Summary

Problems solved by technology

These dictionary learning methods add the difference between categories, but do not consider the local relationship and context information in the data space, resulting in low expression ability of the dictionary

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