Image super-resolution rebuilding method based on dictionary learning and bilateral holomorphy

Abstract

The invention discloses an image super-resolution rebuilding method based on dictionary learning and bilateral holomorphy, and mainly solves the problem of poor quality of rebuilt images through the existing dictionary learning method. The image super-resolution rebuilding method includes: (1), acquiring initial high-definition images; (2), training initial an inner dictionary set d0 and an initial outer dictionary set D0; (3), calculating initial a holomorphy weight matrix W0 on the initial high-definition images; (4), performing holomorphy optimizing processing on the inputted initial high-definition images to acquire optimized images; and (5), applying the initial inner dictionary set d0 and the initial outer dictionary set D0 to rebuild the optimized images to acquire rebuilt images. The image super-resolution rebuilding method can rebuilt natural images, can effectively preserve edges and texture information of the images and can be used for video monitoring and video switching.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image super-resolution reconstruction method, which can be used for super-resolution reconstruction of natural images, remote sensing images and medical images. Background technique [0002] Image super-resolution reconstruction is the inverse problem of reconstructing a high-resolution image from a single or multiple low-resolution images. To solve this problem, some interpolation-based methods and model-based reconstruction methods are proposed. Interpolation methods include nearest neighbor interpolation method and bilinear interpolation method, etc. These methods are simple and fast, but they are easy to cause blurred edges and fail to achieve good reconstruction results; model-based methods include iterative back-projection method, maximum a posteriori probability Although these methods can produce better reconstruction results, the parameters of the reconstructio...

AI Technical Summary

Problems solved by technology

When the selected high-resolution sample image cannot effectively provide the lost information of the image to be super-imaged, the reconstruction effect will decrease, and this type of method ignores the prior knowledge of the image to be super-image itself; in addition, some methods only use the low-resolution image itself Structural similarity achieves resolution improvement. Although the prior information of the superimage itself is fully utilized, this type of method is subject to the strength of the similarity of the superimage itself, and the reconstruction result cannot be guaranteed.

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