A Blind Restoration Method for Fuzzy Variable Image Based on Deep Convolutional Network

Abstract

The present invention discloses a method for blind restoration of blurred variable images based on deep convolution network, comprising the following steps: S1, modeling the blur types that need to be considered in blurred images, and determining the range of blur parameters for each type of blur; S2, According to the blur parameter range of each blur type determined in step S1, synthesize a training data set with variable blur, wherein each set of training data includes clear images, blur images and blur parameters; S3, build and train a deep convolutional network: Input the training data set into the neural network, optimize the weight of the neural network, and obtain the trained deep convolutional network; S4, input the blurred image to be restored into the trained deep convolutional network, and the output is the restored clear image. The method gets rid of the limitation of the traditional deblurring algorithm, and uses a fully convolutional network to estimate a variety of variable blurs, which improves the accuracy and speed of the algorithm.

Description

technical field [0001] The invention relates to the fields of digital image processing and machine learning, in particular to a blind restoration method for fuzzy variable images based on a deep convolutional network. Background technique [0002] When a camera or other optical imaging device is used to image an object, the image is likely to be blurred due to many factors. Insufficient lighting conditions and too long exposure time will cause the relative motion between the camera and the subject to be recorded in the final image, which is what we often call motion blur. In the field of remote sensing photography, atmospheric turbulence will affect the final imaging effect and cause blurring. Another common type of blur stems from inaccurate shooting focus. In most cases, we do not have the conditions to retake a clear image, so it is necessary to perform a deblurring operation on the blurred image to restore a clear image. [0003] The blurring process of an image is us...

AI Technical Summary

Problems solved by technology

Although the above method introduces DNN as a method for estimating kernel parameters and makes good use of the learning ability of DNN for a large amount of data, it still has the following limitations: 1) it does not break away from the traditional clear image restoration method, and the running speed is slow; 2) for The prediction of the fuzzy kernel needs to be designed for different types of networks, which is not universal; 3) For the variable fuzzy kernel, block processing and weighted fusion are required. The block accuracy affects the restoration effect and consumes additional running time

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