Semi-supervised image deblurring method based on fusion attention mechanism

Abstract

The invention provides a semi-supervised image deblurring method based on a fusion attention mechanism, and the method comprises the steps: step 1, obtaining an image training data set, and dividing the image training data set into two training subsets; step 2, constructing an image blurring reduction model based on full supervision and non-supervision, wherein the image blurring reduction model comprises a full supervision network and a non-supervision network; step 3, inputting one subset into a fully supervised network to be trained for learning, and inputting the other subset into an unsupervised network to be trained; step 4, screening image features through the attention mechanism in the network, distributing corresponding weights to feature information extracted by the network, obtaining important image information, carrying out feature fusion on the important image information, and obtaining a final clear image.According to the invention, the attention mechanism and the neural network are combined; a network model for motion blurred image restoration is established, and effective and accurate restoration of the blurred image is realized.

Description

technical field [0001] The invention relates to the technical field of computer vision image restoration, in particular to a semi-supervised image deblurring method based on a fusion attention mechanism. Background technique [0002] Image is the basis of human vision, contains a large number of information elements, and is an important way for people to obtain and communicate information. In recent years, in the process of image acquisition, due to object movement, camera shake and other reasons, there may be loss of details, resulting in the image not being able to transmit information correctly, affecting the quality of the image, and then affecting the subsequent analysis and recognition of the image, such as target recognition , target tracking, etc. In daily life, with the popularization of photographic equipment such as smartphones and cameras, images have become an important way for people to record their lives and transmit information. However, it is difficult for...

AI Technical Summary

Problems solved by technology

More and more deblurring algorithms have been proposed, but the algorithms with good results generally have the problem of high time complexity. Improving the operating efficiency of the algorithm is a major focus of future work

In addition, the fuzzy data set used for training the neural network has a direct impact on the restoration results, but the types and quantities of open source fuzzy data sets are relatively small, and it is difficult to extend the algorithm to image deblurring in real scenes

Images