Research Area:  Machine Learning

Abstract:

Over the last decade, the number of digital images captured per day has increased exponentially, due to the accessibility of imaging devices. The visual quality of photographs captured by low cost or miniaturized imaging devices is often degraded by noise during image acquisition and data transmission. With the re-emergence of deep neural networks, the performance of image denoising techniques has been substantially improved in recent years. The objective of this paper is to provide a comprehensive survey of recent advances in image denoising techniques based on deep neural networks. We begin with a thorough description of the fundamental preliminaries of the image denoising problem, followed by an overview of the benchmark datasets and commonly used metrics for objective assessment of denoising algorithms. We study the existing deep denoisers in the supervised and unsupervised training paradigms and review the technical specifics of some representative methods within each category. We conclude the survey by remarking on trends and challenges in the development of state-of-the-art algorithms and future research.

Keywords:  
Image denoising
Deep learning
Convolution neural networks
Recurrent neural networks
Machine Learning

Author(s) Name:  Saeed Izadi, Darren Sutton & Ghassan Hamarneh

Publisher name:  Springer

DOI:  10.1007/s10462-022-10305-2

Volume Information:  

Paper Link:   https://link.springer.com/article/10.1007/s10462-022-10305-2