Dataset for salt-and-pepper noise image classification, noise marking and denoising

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_J is obtained by adding salt-and-pepper noise with random density to the Pascal VOC dataset. In addition, 91images are converted into grayscale image, image blocks are captured, salt-and-pepper noise with random density is added, and 'noise image-noise mask' pairs are obtained to construct dataset_M. Meanwhile, 'noise image-clean image' pairs are obtained to construct dataset_D.