{"url":"/dataset/bsd","name":"BSD","full_name":"Berkeley Segmentation Dataset","description_markdown":"**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.\r\n\r\nSource: [A Deep Journey into Super-resolution: A Survey](https://arxiv.org/abs/1904.07523)\r\nImage Source: [https://www.slideshare.net/jbhuang/single-image-super-resolution-from-transformed-selfexemplars-cvpr-2015](https://www.slideshare.net/jbhuang/single-image-super-resolution-from-transformed-selfexemplars-cvpr-2015)","description_withheld":null,"homepage":"https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/","introduced_date":"2001-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics","first_author":null,"url":"http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=937655"},"license":{"name":"Custom (research-only, non-commercial, attribution)","url":"https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/#:~:text=Downloads"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Super-Resolution","url":"/task/image-super-resolution","datasets_with_task":"/datasets/task/image-super-resolution"},{"name":"Color Image Denoising","url":"/task/color-image-denoising","datasets_with_task":"/datasets/task/color-image-denoising"},{"name":"Grayscale Image Denoising","url":"/task/grayscale-image-denoising","datasets_with_task":"/datasets/task/grayscale-image-denoising"},{"name":"Image Denoising","url":"/task/image-denoising","datasets_with_task":"/datasets/task/image-denoising"},{"name":"Blind Super-Resolution","url":"/task/blind-super-resolution","datasets_with_task":"/datasets/task/blind-super-resolution"},{"name":"Density Estimation","url":"/task/density-estimation","datasets_with_task":"/datasets/task/density-estimation"},{"name":"Unified Image Restoration","url":"/task/unified-image-restoration","datasets_with_task":"/datasets/task/unified-image-restoration"},{"name":"Salt-And-Pepper Noise Removal","url":"/task/salt-and-pepper-noise-removal","datasets_with_task":"/datasets/task/salt-and-pepper-noise-removal"},{"name":"Compressive Sensing","url":"/task/compressive-sensing","datasets_with_task":"/datasets/task/compressive-sensing"}],"languages":[],"variants":["BSD68 sigma65","BSD68 sigma60","BSD68 sigma55","BSD68 sigma45","BSD68 sigma40","BSD68 sigma20","BSD68 CS=50%","BSDS300","BSDS100 - 8x upscaling","BSDS100 - 4x upscaling","BSDS100 - 2x upscaling","BSD68 sigma75","BSD68 sigma70","BSD68 sigma50","BSD68 sigma5","BSD68 sigma35","BSD68 sigma30","BSD68 sigma25","BSD68 sigma15","BSD68 sigma10","BSD200 sigma70","BSD200 sigma50","BSD200 sigma30","BSD200 sigma10","BSD200 - 2x upscaling","BSD","BSD300 sigma70","BSD300 sigma50","BSD300 sigma30","BSD300 Noise Level 70%","BSD300 Noise Level 50%","BSD300 Noise Level 30%","BSD100 - 8x upscaling","BSD100 - 4x upscaling","BSD100 - 3x upscaling","BSD100 - 2x upscaling","BSD100 - 16x upscaling"],"data_loaders":[],"num_papers_in_archive":718,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-super-resolution-on-bsd100-4x-upscaling","task":"Image Super-Resolution","dataset_variant":"BSD100 - 4x upscaling","rows":71,"metrics":["PSNR","SSIM","MOS","LPIPS","DISTS"],"first_row_in_archive_order":{"model":"DRCT-L","paper":"/paper/drct-saving-image-super-resolution-away-from","metrics":{"PSNR":"28.16","SSIM":"0.7577"},"code_links":[{"title":"ming053l/drct","url":"https://github.com/ming053l/drct"}]},"note":"rows are the archive's own order at snapshot; 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