Multi-view image super-resolution method based on depth information

Abstract

The invention provides a multi-view-point image super-resolution method based on deep information. The multi-view-point image super-resolution method mainly solves the problem that in the prior art, the edge artifact phenomenon occurs when super-resolution reconstruction is conducted on a low-resolution view point image. The method includes the steps of mapping a high-resolution colored image of a view point k on the image position of a view point n according to a pinhole camera model through the deep information, related camera parameters and a backward projection method, conducting effectiveness detection based on pixel mapping relations between the joint view points of the depth differences and the color differences on the projected image, only reserving pixel points conforming to effectiveness detection so as to prevent luminance differences between different view points from influencing illumination regulation conducted on the colored image of the view point k in advance, separating out high-frequency information of the projected image, and adding the high-frequency information to the image obtained after the low-resolution colored image of the view point n is sampled so as to obtain a super-resolution reconstructed image of the view point n. By means of the method, the edge artifact phenomenon is effectively relieved when super-resolution reconstruction is conducted on the low-resolution view point, and the quality of the super-resolution reconstructed image is improved.

Description

technical field [0001] The invention relates to a multi-viewpoint image super-resolution method based on depth information, and belongs to the technical field of image processing. Background technique [0002] At present, the acquisition and playback technology of multi-viewpoint video has made great progress, and 3D stereoscopic video has become a promising application. However, with the development of stereoscopic video, many problems have arisen, such as the increase in the amount of transmitted data and the burden of computational complexity. The solution is to adopt a mixed-resolution multi-view video architecture, down-sample the color images of individual viewpoints at the sending end to obtain a low-resolution form, and restore its high-frequency components through super-resolution technology at the receiving end to effectively reduce the Transfer data volume and computational complexity. How to effectively recover the high-frequency information of the low-resoluti...

AI Technical Summary

Problems solved by technology

The defect of this method is that the reverse projection method is used to detect the validity of the pixel mapping relationship between different viewpoints, only the depth information of the image is used, and edge artifacts will occur due to the inaccurate edge depth of foreground objects; secondly, in order to reverse Projection requires two mappings between viewpoints, which increases the computational complexity

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