Image deblurring: comparison and analysis

Abstract

Technological advancements and the advent of digital devices and media make images an important part of today’s social life. Image blurring is a common challenge that results from multiple factors such as object movement, camera shake, and raindrops, among others. Image deblurring has progressively become an important field of image restoration as directed by research findings. After research for more than five decades, significant research efforts have yielded useful technologies of image deblurring. This article provides an overview of the current knowledge on image deblurring technology by focusing on the classical methods and modern trends in the field. The article reviews the conventional methods and achievements made in past studies using evidence from 34 scholarly articles. The article also examines the application of algorithms in specific deblurring methodologies adopted in recent works. It covers the recent trend of learning-based models used to restore images and their effectiveness. They include Convolutional Neural Networks, Recurrent Neural Networks and Graph Convolutional Networks. Novel deep-learning deblurring techniques are also explored. Based on the findings, issues of concerns, opportunities and direction for future research are provided to advance image deblurring technologies.