{"url":"/dataset/lolv2-synthetic","name":"LOLv2-synthetic","full_name":null,"description_markdown":"To make synthetic images match the property of real dark photography, we analyze the illumination distribution of low-light images. We collect 270 low-light images from public MEF [42], NPE [6], LIME [8], DICM [43], VV,2 and Fusion [44] dataset, transform the imagesT into YCbCr channel and calculate the histogram of Y channel. We also collect 1000 raw images from RAISE [45] as normal-light images and calculate the histogram of Y channel in YCbCr. \r\n\r\nRaw images contain more information than the converted results. For raw images, all operations used to generate pixel values are performed in one step on the base data, making the result more accurate. 1000 raw images in RAISE [45] are used to synthesize low-light images. Interface provided by Adobe Lightroom is used and we try different kinds of parameters to make the histogram of Y channel fit the result in low-light images. Final parameter configuration can be found in the supplementary material. The illumination distribution of synthetic images matches that of low-light images. Finally, we resize these raw images to 400 × 600 and convert them to Portable Network Graphics format.","description_withheld":null,"homepage":"http://39.96.165.147/Pub%20Files/2021/ywh_tip21.pdf","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Low-Light Image Enhancement","url":"/task/low-light-image-enhancement","datasets_with_task":"/datasets/task/low-light-image-enhancement"}],"languages":[],"variants":["LOLv2-synthetic"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/low-light-image-enhancement-on-lolv2-1","task":"Low-Light Image Enhancement","dataset_variant":"LOLv2-synthetic","rows":9,"metrics":["Average PSNR","SSIM","LPIPS"],"first_row_in_archive_order":{"model":"DPEC_","paper":"/paper/resvmunetx-a-low-light-enhancement-network","metrics":{"Average PSNR":"29.95","SSIM":"0.950"},"code_links":[{"title":"wangshuang233/DPEC-VM","url":"https://github.com/wangshuang233/DPEC-VM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/enhancing-low-light-images-with-kolmogorov","title":"Enhancing Low-Light Images with Kolmogorov–Arnold Networks in Transformer Attention","date":"2024-11-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/glare-low-light-image-enhancement-via","title":"GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval","date":"2024-07-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/resvmunetx-a-low-light-enhancement-network","title":"DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image Clarity","date":"2024-06-28","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/you-only-need-one-color-space-an-efficient","title":"You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement","date":"2024-02-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lyt-net-lightweight-yuv-transformer-based","title":"LYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement","date":"2024-01-26","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/global-structure-aware-diffusion-process-for-1","title":"Global Structure-Aware Diffusion Process for Low-Light Image Enhancement","date":"2023-10-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/flight-mode-on-a-feather-light-network-for","title":"FLIGHT Mode On: A Feather-Light Network for Low-Light Image Enhancement","date":"2023-05-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/retinexformer-one-stage-retinex-based","title":"Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement","date":"2023-03-12","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":2,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":11,"samples_ran":3,"samples_unverified":8,"pointer_only_for_licence":1,"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."}