A Survey of Single Image Blind Motion Deblurring from Traditional to Deep Learning

Single image blind motion deblurring, a cornerstone of low-level computer vision, seeks to recover a sharp image from a single blurred observation, addressing challenges posed by motion-induced degradation. This survey provides a comprehensive review of the field, spanning traditional methodologies and deep learning (DL) methods. We begin by defining the problem, outlining its significance, and tracing its research evolution. The article systematically examines traditional approaches–including prior-based, edge-detection, patch-based, and specialized deblurring techniques–followed by an in-depth exploration of DL-based methods, categorized into hybrid model-driven/data-driven frameworks and fully data-driven architectures. Key datasets, loss functions, and quantitative performance evaluations of both classic and state-of-the-art methods on benchmarks are presented to offer practical insights. We conclude by summarizing advancements, identifying persistent challenges such as handling complex real-world data and computational efficiency, and proposing future research directions. This survey serves as a valuable resource for researchers, providing a holistic understanding of blind motion deblurring and fostering innovation in this dynamic domain.