Research On Blurred Image Restoration Algorithm Based On Deep Learning

With the popularity of shooting equipment,people can record scenes in daily life in the form of images.However,due to camera shake or object movement during shooting,the captured image often has motion blur.Therefore,it is very necessary to restore blurred images.The process of blurred image restoration,also known as image deblurring,is an important task in the field of computer vision and image processing.In recent years,the ”end-to-end” deblurring method based on deep learning has become more and more popular.In this paper,we build several deep networks to learn the motion blur image,so as to recover the corresponding sharp image.The main research contents can be divided into the following three parts:(1)To solve the problem that the restoration model is difficult to capture global information,a global awareness generative adversarial network is proposed in this paper.By introducing the global context block to capture the global information,the model can deal with the image blur more easily.In addition,considering the lack of detail information in blurred images,a spatial detail enhancement module is proposed to adaptively learn the spatial information of features,and enhance the information of specific locations,so as to make the image details sharper.Experimental results show that this method not only has high quantitative evaluation results,but also can remove the blur and restore the details well.(2)In order to take advantage of the information contained in the different scale features,a multi-scale feature fusion network is proposed in this paper.Firstly,a crossscale feature fusion module is proposed to fuse features at different resolutions,so as to improve the performance of deblurring.In addition,a multi-scale convolution block is proposed to obtain the local information of different receptive fields.By fusing the features extracted from different convolution kernels,the model can better preserve the image details.Finally,the image is reconstructed on multiple scales,so that the model can predict the restored image more accurately.The experimental results show that the method can not only obtain higher performance,but also have a better visual perception effect.(3)In order to effectively remove the blur in local regions and the global image,this paper proposes a weighted spatial pyramid feature fusion network combined with the first two parts.Firstly,the input image is divided into different regions in the form of a spatial pyramid,so that the model can learn the features of local regions and the global image respectively,and a weighted feature fusion module is proposed to fuse the information in these features.In addition,considering the serious degradation of high-frequency information in the blurred image,this paper also proposes a high-frequency enhancement module to make the details of the image more obvious.The final experimental results show that this method is not only better than the previous two methods in qualitative and quantitative aspects,but also better than other methods.