A super-resolution image reconstruction method of a generative adversarial network based on an attention mechanism

Abstract

The invention discloses a super-resolution image reconstruction method of a generative adversarial network based on an attention mechanism, and the method comprises the steps of preprocessing an ImageNet data set, and manufacturing a training data set corresponding to a high-resolution image and a low-resolution image; constructing a generative adversarial network model for training, and introducing an attention mechanism into the model; inputting the obtained training data set into a generative adversarial network in sequence to carry out model training; and inputting the to-be-processed image into the trained generation network model to obtain a reconstructed high-resolution image. According to the invention, the attention mechanism is added into the perception network to extract the salient region of the target; a mode of combining local information and global information is utilized to enable the generated image to be closer to a real high-resolution image, and the perception lossis introduced to improve the generation effect, so that the edge and the detail information of the reconstructed image are clearer, and the reconstruction effect is better.

Description

technical field [0001] The present invention relates to an image reconstruction method, in particular to a super-resolution image reconstruction method based on a generating confrontation network. Background technique [0002] Image super-resolution reconstruction is a technique that uses low-resolution images to produce high-resolution images. The application field of image super-resolution reconstruction is extremely broad, and it has important application prospects in military, medical, public security, computer vision, etc. [0003] At present, super-resolution reconstruction algorithms can be mainly divided into reconstruction-based methods and learning-based methods. Reconstruction-based methods are based on balanced and unbalanced sampling theorem, assuming that the low-resolution input sample signal can well predict the original high-resolution signal. The learning-based method uses a large number of high-resolution images to construct a learning library to generat...

AI Technical Summary

Problems solved by technology

The main problem is that the edge details of the reconstructed image are insufficient, and the performance of the final image is not good.

The reconstruction process reconstructs the foreground and background to the same extent, resulting in a waste of computing resources

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