In order to improve the efficiency and quality of salt-and-pepper denoising and realize the strategy of ‘denoising after judging’, the noise image classification network (CNN-J) is needed to judge whether the input image is a noisy image. For noisy images, the noise marking network (CNN-M) and noise denoising network (CNN-D) are combined for denoising processing, and the clean image will be directly output. In order to train the above three networks, three datasets are provided here, which are dataset_J, dataset_M and dataset_D, respectively.

Dataset Files

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[1] Chengqiang Huang, "Dataset for salt-and-pepper noise image classification, noise marking and denoising", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/3n2c-qt78. Accessed: Dec. 06, 2024.
@data{3n2c-qt78-24,
doi = {10.21227/3n2c-qt78},
url = {http://dx.doi.org/10.21227/3n2c-qt78},
author = {Chengqiang Huang },
publisher = {IEEE Dataport},
title = {Dataset for salt-and-pepper noise image classification, noise marking and denoising},
year = {2024} }
TY - DATA
T1 - Dataset for salt-and-pepper noise image classification, noise marking and denoising
AU - Chengqiang Huang
PY - 2024
PB - IEEE Dataport
UR - 10.21227/3n2c-qt78
ER -
Chengqiang Huang. (2024). Dataset for salt-and-pepper noise image classification, noise marking and denoising. IEEE Dataport. http://dx.doi.org/10.21227/3n2c-qt78
Chengqiang Huang, 2024. Dataset for salt-and-pepper noise image classification, noise marking and denoising. Available at: http://dx.doi.org/10.21227/3n2c-qt78.
Chengqiang Huang. (2024). "Dataset for salt-and-pepper noise image classification, noise marking and denoising." Web.
1. Chengqiang Huang. Dataset for salt-and-pepper noise image classification, noise marking and denoising [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/3n2c-qt78
Chengqiang Huang. "Dataset for salt-and-pepper noise image classification, noise marking and denoising." doi: 10.21227/3n2c-qt78