| The Motion blur is a widespread phenomenon.The essential reason is the relative movement between the picture taken in the exposure time and the image sensor of the camera.Blur makes some valuable photos lose important information.People need to recover clear images from blurred images for useful information.Therefore,it is necessary to study a highly efficient deblurring method.Blind image restoration methods for degraded motion images mostly use gray image.Converting color images into gray images may results in the loss of information because color image has different image layers.The computational speed of the variational energy equation is fast and the convergence effect is well.The energy equation is composed of data terms and regularization,and the split-Bregman algorithm is introduced in the solution.In order to overcome the loss of information,a blind restoration method for motion blurred color images is proposed in this paper.The blurred kernel function is quickly obtained by input of a single motion blurred image,and the clear image is calculated.According to the requirements of color image,one is the MTV model and the normalized hyper Laplacian prior term,the other is the CTV model and the normalized hyper Laplacian prior term and the curvature and the normalized hyper Laplacian prior term,three kinds of model for blind image restoration.Under the multi-scale,we estimate kernel from coarse scale to the fine scale of each channel of color image respectively,finally put all the channels on the restoration of image synthesis,They can avoid the information loss,and preserve the edge effectively.Citing the normalized hyper Laplasse distribution as a regularization,Although models will increase the complexity of computation,experiments demonstrate the feasibility and validity of the proposed method. |
