A Super-resolution Reconstruction Method for Joint Semantic Segmentation

Abstract

The present invention proposes an image super-resolution reconstruction method combined with semantic segmentation, which uses the intermediate results and final results of low-quality images generated during semantic segmentation to perform super-resolution reconstruction, and performs large-scale super-resolution reconstruction time can have a more realistic effect. As the high-level semantic information of the image is inherent information of the image, it contains a large number of category priors at the pixel level, so it can be used as the constraint information in the process of super-resolution reconstruction to improve the quality of its reconstruction results. The present invention combines image super-resolution reconstruction, a low-level problem of computer vision, with image semantic segmentation as a high-level problem, and uses various information generated after the image is semantically segmented to constrain and coordinate the process of super-resolution reconstruction. Enhancement solves the problem of lack of authenticity in the reconstruction of low-resolution images under the condition of large zoom factors, and has a higher improvement in subjective quality evaluation.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for image super-resolution reconstruction using semantic segmentation. Background technique [0002] Image super-resolution reconstruction refers to the use of various technical means to convert low-resolution images into high-resolution images, recover more high-frequency information, and make the image have clearer texture and details. The image super-resolution reconstruction method has been developed for half a century since it was first proposed. Many image super-resolution reconstruction methods can be roughly divided into three categories according to their principles: interpolation-based methods, reconstruction-based methods, and learning-based methods. Methods. [0003] Interpolation-based methods link the super-resolution reconstruction problem with the image interpolation problem and are the most straightforward methods in super-resolution reconstruc...

AI Technical Summary

Problems solved by technology

[0006] Although today's single image super-resolution reconstruction technology relies on deep learning to achieve great breakthroughs in accuracy and speed, its effect will decline when dealing with more complex low-resolution images

For example: when the processed low-resolution image contains many objects and there is a large part of overlap and occlusion between the objects, the existing methods cannot well divide the boundary between the overlapping and occlusion objects, resulting in the texture of the reconstruction result Insufficient detail, even multiple overlapping objects will be reconstructed as one

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