Research On Blind Image Deblurring Methods Based On Deep Learning

Image deblurring refers to the process of restoring a blurry image to a higher level of sharpness.It has been widely studied in the field of computer vision.Current deep learning-based deblurring methods often suffer from complex network structures and high computational costs.In this paper,we propose three deep learning methods for blind image deblurring tasks.The specific research contents are as follows:Firstly,we propose a blind image deblurring method based on contextual Transformer.The Transformer,which can fully utilize the contextual information of an image,is used as the basic module.By incorporating a high-quality image restoration module at the end of the network,fine textures and high-resolution features can be effectively recovered,resulting in high-quality image restoration.Comparative experimental results demonstrate that this method can effectively improve the quality of restored images.Secondly,we propose a blind image deblurring method based on fast Fourier transform(FFT).We introduce frequency domain methods into the image deblurring network.By performing fast Fourier transform on the features and extracting information differences between high-frequency and low-frequency domains,we fuse information from different frequency domains.Moreover,we enhance the feature extraction capability by adding a feature enhancement module at the end of the network,which expands the receptive field range.Comparative experimental results show that this method effectively improves the performance of the deblurring network.Finally,we propose a blind image deblurring method based on a multi-scale encoder-decoder network.We adopt a U-shaped encoder-decoder architecture and input blurred images of different scales into the deblurring network to obtain deblurred images at different scales.We introduce dilated convolutions in the residual blocks that constitute the encoder and decoder to expand the receptive field.Additionally,we incorporate an attention-based feature fusion module in between the encoder and decoder to effectively fuse multi-scale features.Experimental results demonstrate that this method not only achieves fast deblurring but also enhances the performance of image deblurring.