Blind removal of image motion blur based on gradient domain and depth learning

Abstract

The invention discloses an image motion blur blind removing method based on gradient domain and depth learning, A guide filter gradient domain image is used as a basic image, A L0 filt gradient domainimage and a corresponding clear image are taken as samples, that clear image is randomly convolution with different blur kernel, and 1% Gaussian white noise is added to generate a motion blur image.The gradient domain image after the guide filter, the gradient domain image after the L0 filter and the motion blur image constitute a training data set; The depth convolution neural network is constructed, and the weight data of the depth convolution neural network is learned from the training data set, and the depth convolution neural network used for motion fuzzy kernel estimation is learned. Extracting the weight data of the network training, obtaining the motion blur kernel, optimizing the deconvolution function of the image prior constraint, and obtaining the motion blur image by using the total variational term. This method can effectively suppress the image ringing effect and reduce the image noise, and the effect of motion blur removal is better.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for blindly removing image motion blur based on gradient domain and deep learning. Background technique [0002] In the process of image acquisition, transmission, recording, etc., the actual image may be degraded to varying degrees due to factors such as harsh actual environment, imperfect imaging equipment, and relative movement between the equipment and the target, such as noise, blur, and geometric distortion. These will have a great impact on digital images. Image blur can be caused by certain reasons, such as Gaussian blur caused by atmospheric turbulence, motion blur caused by relative motion during the exposure of the camera, and defocus blur caused by the subject being outside the focal length of the camera when shooting, etc. Therefore, it is very important to remove the blur and get a clear image. [0003] There are two main categories of existing i...

AI Technical Summary

Problems solved by technology

Deep learning networks are mainly used in image restoration. At present, image deblurring methods based on deep learning networks mostly ignore the high-frequency information of images, so that the texture details of images are lost in the process of deblurring, which not only takes a long time, but also Will cause the ringing effect of the image to be more obvious

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