Image deblurring method based on ADMM neural network

Abstract

The invention discloses an image deblurring method based on an ADMM neural network. Modeling is carried out for the problem of image deblurring. Split item solving is carried out on the problem by utilizing an ADMM algorithm; the related neural network is constructed according to the three solved sub-problems, the ADMM algorithm parameters and the related regular terms which better accord with thereality are obtained through training of the constructed neural network, the workload of participation in regular term selection can be reduced, and the actual effect and the operation efficiency canbe further improved. According to the method, the excellent theoretical basis of a traditional optimization algorithm is reserved, the trainability of the neural network is utilized, and deblurring processing can be well carried out on the moving image.

Description

technical field [0001] The method of the invention relates to an image deblurring method aimed at image features, in particular to an image deblurring method based on ADMM neural network. Background technique [0002] In recent years, due to the vigorous development of image processing and deep learning, a large number of image deblurring algorithms have emerged. The existing popular image deblurring algorithms are mainly divided into end-to-end image deblurring algorithms based on neural networks and image deblurring algorithms based on optimization theory. The advantage of the end-to-end defuzzification algorithm based on the neural network is that it does not need to establish a more complex mathematical solution model, but only needs to follow the principles of deep learning. The adjustment of relevant parameters in the training process mainly relies on the back propagation mechanism and the optimizer. It does not require human intervention. The disadvantage is that man...

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Problems solved by technology

The advantage of the end-to-end defuzzification algorithm based on the neural network is that it does not need to establish a more complex mathematical solution model, but only needs to follow the principles of deep learning. The adjustment of relevant parameters in the training process mainly relies on the back propagation mechanism and the optimizer. It does not require human intervention. The disadvantage is that many neural networks with better effects are obtained through a large number of experiments, and their essence cannot be explained by relevant theories. Secondly, the training of neural networks is mainly to learn the potential laws of the data set. If the data set If it does not contain a certain necessary underlying law, then the effect will vary greatly in the application of different scenarios, that is, there is an over-fitting problem

The advantage of the image deblurring algorithm based on optimization theory is that it can be used without training, which can avoid the over-fitting problem of the neural network, so the effect is not much different in the application of different scenarios. The disadvantage is that it needs to establish a corresponding solution model. Not only does it require relevant personnel to have a lot of prior knowledge, but the key parameters in the algorithm usually need to be obtained through a lot of human experiments, otherwise it will greatly affect the actual effect

So far, there is no relevant report on the fusion of the advantages of neural network-based algorithms and optimization theory-based algorithms for image deblurring

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