Image deblurring method based on aggregation expansion convolutional network

Abstract

The invention belongs to the technical field of computer digital image processing, and particularly relates to an image deblurring method based on an aggregation expansion convolutional network. The method comprises the steps of constructing a deep neural network, generating a network based on a condition countermeasure, wherein the network comprises a generator and a discriminator, the generatorstructure uses a stacked self-encoder module, and the self-encoder module is connected with a jump through a self-encoder structure, a residual error module is used on a construction module, residualerror module uses a residual network and multi-channel aggregation expansion convolution, and the discriminator uses a five-layer convolutional neural network; training the deep neural network: usingfuzzy image data set in public and real scenes, using image content loss function and a countermeasure loss function to train the deep neural network constructed in the previous step, and using a trained network model to carry out deblurring processing on a blurred image. According to the method disclosed by the invention, the deblurring effect can be ensured, a blurred image can be quickly and efficiently restored to a clear image, and the image deblurring efficiency can be greatly improved.

Description

technical field [0001] The invention belongs to the technical field of computer digital image processing, and in particular relates to an image deblurring method based on an aggregation expansion convolution network. Background technique [0002] Image blur is a common problem when taking photos, especially when shooting with lightweight devices such as mobile phones. Relative motion between the camera and the object, including camera shake and object motion, is the main cause of blur. Because the motion of different objects is usually different from each other, the degree of blur on the image is usually not spatially uniform. Moreover, the depth variation of the scene and the segmentation boundary of the object will make the blurring more complicated. Motion blur degrades image quality and affects the effectiveness of many image processing algorithms. Standard network models trained only on high-quality images degrade significantly when applied to blurry images caused by...

AI Technical Summary

Problems solved by technology

This method deblurs better and takes less time than the previous method, but still requires a lot of memory resources due to the dense architecture used

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