One of the weak points of most of denoising algoritms (deep learning based ones) is the training data. Due to no or very limited amount of groundtruth data available, these algorithms are often evaluated using synthetic noise models such as Additive Zero-Mean Gaussian noise. The downside of this approach is that these simple model do not represent noise present in natural imagery.

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[1] Alexandra Malyugina, Nantheera Anantrasirichai, David Bull, "BVI-Lowlight-Images", IEEE Dataport, 2022. [Online]. Available: http://dx.doi.org/10.21227/zp7a-0683. Accessed: Apr. 17, 2024.
@data{zp7a-0683-22,
doi = {10.21227/zp7a-0683},
url = {http://dx.doi.org/10.21227/zp7a-0683},
author = {Alexandra Malyugina; Nantheera Anantrasirichai; David Bull },
publisher = {IEEE Dataport},
title = {BVI-Lowlight-Images},
year = {2022} }
TY - DATA
T1 - BVI-Lowlight-Images
AU - Alexandra Malyugina; Nantheera Anantrasirichai; David Bull
PY - 2022
PB - IEEE Dataport
UR - 10.21227/zp7a-0683
ER -
Alexandra Malyugina, Nantheera Anantrasirichai, David Bull. (2022). BVI-Lowlight-Images. IEEE Dataport. http://dx.doi.org/10.21227/zp7a-0683
Alexandra Malyugina, Nantheera Anantrasirichai, David Bull, 2022. BVI-Lowlight-Images. Available at: http://dx.doi.org/10.21227/zp7a-0683.
Alexandra Malyugina, Nantheera Anantrasirichai, David Bull. (2022). "BVI-Lowlight-Images." Web.
1. Alexandra Malyugina, Nantheera Anantrasirichai, David Bull. BVI-Lowlight-Images [Internet]. IEEE Dataport; 2022. Available from : http://dx.doi.org/10.21227/zp7a-0683
Alexandra Malyugina, Nantheera Anantrasirichai, David Bull. "BVI-Lowlight-Images." doi: 10.21227/zp7a-0683