A Comprehensive Survey on Deep Neural Image Deblurring
It provides a comprehensive review for researchers in computer vision, but it is incremental as it summarizes existing work without new experimental results.
This paper surveys recent advances in deep neural network architectures for both blind and non-blind image deblurring, outlining popular structures, performance metrics, and datasets while discussing current challenges and future research directions.
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. Traditionally, prior-based optimization approaches predominated in image deblurring, but deep neural networks recently brought a major breakthrough in the field. In this paper, we comprehensively review the recent progress of the 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.