High-resolution dictionary based sparse representation image super-resolution reconstruction method

Abstract

The invention discloses a high-resolution dictionary based sparse representation image super-resolution reconstruction method. The method comprises the following steps of: (1) constructing a high-resolution brightness image library; (2) generating a sample training set; (3) learning an over-complete dictionary; (4) primarily establishing a high-resolution image brightness space; (5) establishing an image sample test set; (6) updating the high-resolution image brightness space; (7) calculating a weight sparse matrix; (8) reupdating the high-resolution image brightness space; (9) judging whether to repeat execution; and (10) outputting a high-resolution image. The high-resolution over-complete dictionary learned by the invention can be applied to different amplification factors. Sparse representation, non-local prior and data fidelity constraint are fully utilized, so that local information and global information can be comprehensively utilized. The method has higher super-resolution capacity; and the reconstructed image is closer to an actual image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a super-resolution reconstruction method of a single-frame color image based on machine learning and sparse representation in the fields of medical diagnosis, video monitoring, and HDTV imaging. Background technique [0002] In the fields of medical diagnosis, video surveillance, and high-definition television HDTV imaging, a single-frame image super-resolution reconstruction method that reconstructs a high-resolution image from a low-resolution image is used to improve image resolution. The current single-frame image super-resolution reconstruction technology is mainly based on low-resolution-high-resolution image block pairs to learn a data pair to achieve single-frame image super-resolution reconstruction. [0003] This kind of single-frame super-resolution reconstruction technology based on low-resolution-high-resolution data is also called super-resolution rec...

AI Technical Summary

Problems solved by technology

[0004] To sum up, although the existing technology of super-resolution reconstruction based on instance learning can effectively realize the super-resolution reconstruction of a single frame image, the learned low-resolution-high-resolution data pair is only for a specific magnification, and the data Must be relearned as magnification changes

At the same time, the reconstruction results of the above two methods for the divided structurally similar blocks may be quite different.

In addition, although these two methods can reconstruct high-frequency information to a certain extent by using the local information of the image block, the reconstructed high-resolution image cannot be consistent with the original low-resolution input image after being degraded and reduced, which makes the edges and textures of the reconstructed image insufficient. Clear even contradicts the real edge texture

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