BSD is a dataset used frequently for image denoising and super-resolution. Of the subdatasets, BSD100 is aclassical image dataset having 100 test images proposed by Martin et al.. The dataset is composed of a large variety of images ranging from natural images to object-specific such as plants, people, food etc. BSD100 is the testing set of the Berkeley segmentation dataset BSD300.
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The Urban100 dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models. Image Source: http://vllab.ucmerced.edu/wlai24/LapSRN/
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Color BSD68 dataset for image denoising benchmarks is part of The Berkeley Segmentation Dataset and Benchmark. It is used for measuring image denoising algorithms performance. It contains 68 images.
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The McMaster dataset is a dataset for color demosaicing, which contains 18 cropped images of size 500×500.
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the dataset contains data about hydrogen storage in metal hydrides
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The CELL benchmark is made of fluorescence microscopy images of cell.
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A dataset of color images corrupted by natural noise due to low-light conditions, together with spatially and intensity-aligned low noise images of the same scenes.
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Synthetic training set: This set is constructed in the following two steps and will be used for estimation/training purposes. i) 84,000 275 pixel x 400 pixel ground-truth fingerprint images without any noise or scratches, but with random transformations (at most five pixels translation and +/-10 degrees rotation) were generated by using the software Anguli: Synthetic Fingerprint Generator. ii) 84,000 275 pixel x 400 pixel degraded fingerprint images were generated by applying random artifacts (blur, brightness, contrast, elastic transformation, occlusion, scratch, resolution, rotation) and backgrounds to the ground-truth fingerprint images. In total, it contains 168,000 fingerprint images (84,000 fingerprints, and two impressions - one ground-truth and one degraded - per fingerprint).
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