Image Deblurring Application via Statistical Machine Learning and Deep Learning

Image deblurring tries to eliminate degradation elements of an image causing blurriness and improve the quality of an image for better texture and object visualization. Blind image deblurring is one type of image deblurring that occurs when there is no information about blur effect. Such a deblurring process handles an ill-posed problem, so it requires imposing restrictions on a latent image or a blur kernel that represents blurriness. Various algorithms have been proposed to restore a latent image out of a blurred one, including prior-based optimization methods and, most recently, deep neural networks. Different from recent studies in prior-based optimization methods that impose some priors on the latent image, we regulate the structure of the blur kernel and demonstrate the benefits of imposing a structure on kernels. For modeling a kernel, we propose a kernel mixture structure with the Gaussian kernel as a base kernel. Albeit simple, by combining multiple Gaussian kernels with enhanced structures in terms of scales and centers, the kernel mixture becomes capable of modeling nearly non-parametric shape of blurriness. Recently, deep neural networks brought a major breakthrough in the image deblurring domain. Hence, we comprehensively review the recent progress of deep neural architectures in both blind and non-blind image deblurring. We outline the most popular deep neural network structures used in deblurring applications, describe their strengths and novelties, summarize performance metrics, and introduce broadly used datasets. In addition, we discuss the current challenges and research gaps in this domain and suggest potential research directions for future works. Comprehending the current models and research gaps in deep neural image deblurring networks, we propose a novel blur extraction module (BEM) to extract blur and semantic information and improve the feature extraction procedure. Our proposed module has two sub-modules: cascaded dense dilated convolution (CDDC) block, which is proposed to capture the multi-scale semantic information, and residual integrated poolings (RIPS) block introduced to extract the dynamic blur context. We trained and evaluated the network with our proposed module based on well-known image deblurring datasets. Our results quantitatively and qualitatively show the superiority of our blur extraction module in comparison with state-of-the-art neural image deblurring networks.