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Image denoising in the deep learning era

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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.

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Notes

  1. independent and identically distributed

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Acknowledgements

For their funding support, we are grateful to the Natural Sciences and Engineering Research Council of Canada (NSERC), and its Discovery and Collaborative Health Research Projects (CHRP) programs, and the Canadian Institutes of Health Research (CIHR). We also thank Compute Canada and NVIDIA for the provision of computational resources.

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    Saeed Izadi, Darren Sutton & Ghassan Hamarneh

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Izadi, S., Sutton, D. & Hamarneh, G. Image denoising in the deep learning era. Artif Intell Rev 56, 5929–5974 (2023). https://doi.org/10.1007/s10462-022-10305-2

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