Recent progress in digital image restoration techniques: A review

Abstract

Digital images are playing a progressively important role in almost all the fields such as computer science, medicine, communications, transmission, security, surveillance, and many more. Digital images are susceptible to a number of distortions due to faulty imaging instruments, transmission channels, atmospheric and environmental conditions, etc. resulting in degraded images. Degradation can be of different types such as noise, backscattering, low saturation, low contrast, tilt, spectral absorption, blurring, etc. The degradation reduces digital images' effectiveness and therefore needs to be restored. In this paper, we present an extensive review of image restoration tasks. It addresses problems like image deblurring, denoising, dehazing and super-resolution. Image restoration is fundamentally an image processing problem, but deep learning techniques, based mainly on convolutional neural networks have received a lot of attention in almost all areas of computer science. Along with deep learning, other machine learning methods have also been tried for restoring digital images. In this review, we have therefore categorized digital image restoration techniques as either image processing-based, machine learning-based or deep learning-based. For each category, a variety of approaches presented in recent years have been reviewed. This review also includes a summary of the data sets used for image restoration along with a baseline reference that can be used by future researchers to compare and improve their results. We also suggest some interesting research directions for future work in this area.