Image Denoising: The Deep Learning Revolution and Beyond—A Survey Paper
importance 5/10
solved
Image and Signal Denoising Methods image processing stage: research
Problem
Removing additive white Gaussian noise from images, a decades-old problem thought by some to be exhausted before deep learning revived it.
Why it matters
Denoising underpins quality in medical, scientific and consumer imaging pipelines.
What the AI did
A survey describing how deep learning methods took the lead in noise suppression and broadened the scope of denoising problems treated.
Results
not stated in source
Impact
Evidence supports that deep learning now leads denoising performance, per this peer-reviewed survey.
Record
| Venue | SIAM Journal on Imaging Sciences |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Michael Elad, Bahjat Kawar, Gregory Vaksman |
| Citations | 151 (OpenAlex, at import time) |
| References | 199 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1137/23m1545859 |
| Open access | green |
| Keywords | image denoising, deep learning, survey, Gaussian noise, image processing |
Importance rating
5
real but narrower advance
Denoising is a foundational, decades-studied image processing task that deep learning now leads, with broader roles in inverse problems and diffusion synthesis; no scale stated.
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