Face super-resolution method and system based on fusion attention mechanism

Abstract

The invention discloses a face super-resolution method and system based on a fusion attention mechanism, and belongs to the field of face image super-resolution. The method comprises the steps of down-sampling a high-resolution face image into a target low-resolution face image, carrying out partitioning operation, separating out image blocks which are overlapped with each other, and extracting superficial features by using a superficial feature extractor; fusing features of pixel, channel and space triple attention modules, and enhancing reconstructed face structure details; constructing a fusion attention network as a deep feature extractor, inputting the superficial facial features into the fusion attention network to obtain deep features, wherein the fusion attention network includes a plurality of fusion attention groups, each fusion attention group includes a plurality of fusion attention blocks; and performing up-sampling on the deep feature map, and reconstructing the face feature map subjected to up-sampling into a high-resolution face image of the target. The method is superior to other latest face image super-resolution algorithms, and face high-resolution images with higher quality can be generated.

Description

technical field [0001] The invention belongs to the field of computer vision human face super-resolution, and more specifically relates to a method and system for human face super-resolution based on a fusion attention mechanism. Background technique [0002] Face super-resolution (face hallucination) is a special field of super-resolution (Super-Resolution, SR), which is a method to infer high-resolution images from input low-resolution (Low Resolution, LR) face images. (High Resolution, HR) image technology, which can significantly enhance the details of low-resolution face images. In real-world surveillance scenarios, the distance between the imaging sensor and the face is often too large, resulting in low-resolution face images. Using face super-resolution to restore high-resolution face images is helpful for target person identification. This method plays an important role in many applications such as face detection, face recognition and analysis. [0003] In general...

AI Technical Summary

Problems solved by technology

[0006] In view of the above defects or improvement needs of the prior art, the present invention proposes a face super-resolution method and system based on a fusion attention mechanism, which solves the problem that the current face super-resolution reconstruction algorithm cannot use multiple attention features at the same time, Technical issues that cause limitations in face image reconstruction performance

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