Non-uniform image motion blur removing method based on deep neural network

Abstract

The invention discloses a non-uniform image motion blur removing method based on a deep neural network. The non-uniform image motion blur removing method comprises two main steps of training deep neural network parameters, and estimating and removing motion blur by applying a deep network to an image. The deep network structure consists of a convolution network layer and a forward network layer; the network model training process comprises the following steps: generating a blur image block and a blur kernel pair by using a natural image, and training the deep neural network model parameters by using the data; providing a motion blur image, decomposing the image into an image block set with overlapped areas, inputting each image block into motion blur probability distribution corresponding to deep neural network output image blocks, and further acquiring different motion blur kernels per pixel points of the image by optimizing a Markov random field model; finally, acquiring a blur removed image by using a deconvolution algorithm based on the estimated motion blur kernels. Due to good learning capability of the deep neural network, non-uniform motion blur of the image can be precisely estimated, and clear images can be further obtained.

Description

Technical field: [0001] The invention belongs to the field of image processing, and in particular relates to a method for removing non-uniform motion blur in images based on a deep neural network, which is used for automatically estimating and removing non-uniform motion blur in images. Background technique: [0002] Image deblurring is an important problem in the field of image processing. Existing methods mostly focus on removing globally consistent blur. In addition, there is non-uniform motion blur, that is, the motion blur kernel is spatially inconsistent. This type of blur mostly occurs when objects move during image capture, or when shooting external scenes in a moving car. This problem is very challenging. Existing approaches to address image blur and non-uniform blur estimation and removal are presented below. [0003] 1. Image Uniform Blur Estimation and Removal Method [0004] The current uniform blur kernel estimation algorithm is usually implemented by opti...

AI Technical Summary

Problems solved by technology

This method mainly focuses on removing non-uniform blur of images caused by camera motion, object motion, etc.; at the same time, as a general method, it can also solve the problem of blur removal of uniform blur images

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