Super-resolution image reconstruction method and system based on multi-feature learning

Abstract

The present disclosure discloses a super-resolution image reconstruction method and system based on multi-feature learning. The method makes full use of the rich information contained in a single input image itself for reconstruction without relying on external databases. The method establishes the mapping relationship between image features based on the cross-scale similarity of the image, and directly uses the mapping relationship to reconstruct the high-resolution image containing high-frequency information from the input image, which effectively overcomes the high-frequency information caused by using the interpolation amplification method to reconstruct the image. Missing defects. The method uses singular value thresholding to obtain effective high-frequency information, and uses the gradient feature mapping relationship to amplify the high-frequency information and superimpose it into blocks on the high-resolution image to obtain the final image reconstruction result. This method of using a combination of image features to reconstruct images effectively suppresses noise points in the reconstructed image, and better maintains image edge and texture information, achieving image detail enhancement.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, and in particular, to a method and system for super-resolution image reconstruction based on multi-feature learning. Background technique [0002] The statements in this section merely enhance the background related to the present disclosure and do not necessarily constitute prior art. [0003] Image super-resolution technology is to convert a low resolution image (Low Resolution Image, LR) into a high resolution image (High Resolution Image, HR) by certain means. In this process, the effective information of the image is preserved as much as possible, including the structure, texture, details, etc. of the image. [0004] At present, there are many research methods of image super-resolution technology, which are mainly divided into interpolation-based super-resolution methods, reconstruction-based super-resolution methods and learning-based super-resolution methods. [0005] T...

AI Technical Summary

Problems solved by technology

Images reconstructed by this type of method are prone to blurred edges and poor overall visual effects.

In order to improve the defects caused by traditional interpolation methods, many new interpolation methods have emerged. Although these methods effectively overcome the defects of traditional interpolation methods, it is still difficult to achieve satisfactory visual effects. For example, the edge-based image interpolation method in the image Deformation and other phenomena appear in the details and the algorithm complexity is high

[0006] The second category: reconstruction-based methods, the reconstruction results of this type of method often lack image high-frequency information, and the results often rely on reconstruction constraints, so that the image looks smoother

However, the algorithm will produce blocks in high-frequency information areas such as image details and textures.

This is because the high-frequency subbands searched by the algorithm contain pseudo-pixels, and the pseudo-pixels are placed into the result image during the superposition process, resulting in blocky images in the reconstructed result image.

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