Research On Multiple Degraded Image Restoration And Quality Assessment Of The Restored Image

In recent years,image restoration has always been the focus of digital image processing.Among them,the research on multiple degraded image restoration is a hot and difficult task.In this thesis,we firstly propose a deep convolutional neural network to solve the image restoration problem under a single degradation factor,and then extend it to a multiple degraded image restoration system for denoising,deblurring and image super-resolution.Finally,an image quality assessment algorithm based on regions of interest is proposed to evaluate the quality of reconstructed images.For single factor degraded image restoration,an end-to-end deep convolutional neural network based on dense blocks is used to learn the map function from degraded images to sharp images.To handle the gradient-vanishing problem during the training process,local and global connections are proposed,which improve the flow of gradient and features.At the same time,local and global connections make the restoration network focusing on restoring the missing information of degraded images in the way of residual learning,which ensures the high efficiency of the restoration network.For multiple degraded image restoration,we propose a feasible solution based on the single factor image restoration model and image priors to solve the multiple degraded image restoration problem.The prior information of noise level and blur kernel is obtained through prior estimation methods.Then,priors are reduced and conversed to prior images which have the same size as degraded images by dimension transformation.Finally,all prior images and degraded images are combined to train a image restoration model.For image quality assessment,this thesis firstly combines image saliency and edge intensity to calculate regions of interest and then crops image patches from structure components and texture components of degraded images.After that,we propose a dual-network to evaluate qualities of all those patches.Finally,all the quality scores are weighted to obtain the score of the whole degraded image.Abundant experiments on public datasets show that the proposed model is reasonable and effective.In addition,a series of experiments on the SJTU dataset demonstrate the feasibility of the proposed multiple degraded image restoration system.Finally,we also demonstrate the accuracy of our image quality assessment algorithm by experimental results on public datasets and show the effectiveness for restored images' quality assessment.