Image super resolution reconstruction method and device based on dictionary matching

Abstract

The invention provides an image super resolution reconstruction method and a device based on dictionary matching. A matching dictionary is built, a to-be-reconstructed image is inputted to a multilayer line filter network, local features of the to-be-reconstructed image are extracted, local features of a low-resolution image block with the highest similarities with the local features of the to-be-reconstructed image are searched from the matching dictionary, residual of a joint sample for the local features of the low-resolution image block with the highest similarities is searched in the matching dictionary, interpolation amplification is carried out on the local features of the low-resolution image block with the highest similarities, and with the addition of the residual, a high-resolution image block after reconstruction is acquired. As the local features of the to-be-reconstructed image are extracted via the multilayer line filter network, the precision is higher, the matching degree is higher when matching with the matching dictionary is carried out subsequently, and the reconstructed image has a good quality. Thus, the quality of the reconstructed high-resolution image can be greatly enhanced.

Description

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AI Technical Summary

Benefits of technology

This patented technology provides an algorithm that uses dictionaries (a set of rules) to search through images based on their content or structure. It then finds matches between these patterns found within each frame and adds them back together creating a more accurate version of what was captured earlier. These techniques help enhance the resolution and clarity of digital photography videos while improving overall performance.

Problems solved by technology

This patented technical solution describes two types of techniques for increasingly precise and accurate edge detection in low-quality images: 1 ) interpolating or shading algorithms that use discrete values from previously generated pixels; 2） training dictionaries that can accurately match lower-density versions of these pixel values without sacrificing their sharpness.

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