{"url":"/dataset/i-haze-1","name":"I-HAZE","full_name":null,"description_markdown":"The I-Haze dataset contains 25 indoor hazy images (size 2833×4657 pixels) training. It has 5 hazy images for validation along with their corresponding ground truth images.\n\nSource: [Single image dehazing for a variety of haze scenarios using back projected pyramid network](https://arxiv.org/abs/2008.06713)\nImage Source: [https://data.vision.ee.ethz.ch/cvl/ntire18//i-haze/](https://data.vision.ee.ethz.ch/cvl/ntire18//i-haze/)","description_withheld":null,"homepage":"https://data.vision.ee.ethz.ch/cvl/ntire18//i-haze/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/i-haze-a-dehazing-benchmark-with-real-hazy","title":"I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images","first_author":"Codruta O. Ancuti","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Dehazing","url":"/task/image-dehazing","datasets_with_task":"/datasets/task/image-dehazing"},{"name":"Single Image Dehazing","url":"/task/single-image-dehazing","datasets_with_task":"/datasets/task/single-image-dehazing"},{"name":"SSIM","url":"/task/ssim","datasets_with_task":"/datasets/task/ssim"}],"languages":[],"variants":["I-Haze","I-HAZE"],"data_loaders":[],"num_papers_in_archive":41,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-dehazing-on-i-haze","task":"Image Dehazing","dataset_variant":"I-Haze","rows":4,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"EDN-GTM","paper":"/paper/a-novel-encoder-decoder-network-with-guided","metrics":{"PSNR":"22.90","SSIM":"0.8270"},"code_links":[{"title":"tranleanh/edn-gtm","url":"https://github.com/tranleanh/edn-gtm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sad-net-a-full-spectral-self-attention-detail","title":"SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing","date":"2025-04-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/revitalizing-convolutional-network-for-image","title":"Revitalizing Convolutional Network for Image Restoration","date":"2024-06-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-novel-encoder-decoder-network-with-guided","title":"A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing","date":"2022-02-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/single-image-dehazing-for-a-variety-of-haze","title":"Single image dehazing for a variety of haze scenarios using back projected pyramid network","date":"2020-08-15","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}