{"url":"/dataset/lolv2","name":"LOLv2","full_name":null,"description_markdown":"The real captured dataset of LOL contains 500 low/normallight image pairs. Most low-light images are collected by changing exposure time and ISO, while other configurations of the cameras are fixed. We capture images from a variety of scenes, e.g., houses, campuses, clubs, streets.\r\n\r\nSince camera shaking, object movement, and lightness changing may cause misalignment between the image pairs, inspired by [41], a three-step shooting strategy is used to eliminate such misalignments between the image pairs in our dataset. For one scene, we first shoot two normal-light images $N_1$ and $N_2$. Then, we change the exposure time and ISO to capture a series of low-light images. Finally, we set the exposure time and ISO back to shoot another two normal-light images $N_3$ and $N_4$. The average of $N_i (i = 1,2,3,4)$ is treated as the ground-truth $G=\\frac{1}{4}\\sum^4_{i=1}N_i$. Then, we check\r\nwhether there is object or camera movement. Specifically, the misalignment for these normal-light images is measured by $M=\\frac{1}{4}\\sum^4_{i=1}MSE(Ni, G)$. If M > 0.1, we abandon the corresponding pair.\r\n\r\nThese raw images are resized to 400 × 600 and converted to Portable Network Graphics format. The dataset is publicly\r\navailable.","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"],"data_loaders":[],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/low-light-image-enhancement-on-lolv2","task":"Low-Light Image Enhancement","dataset_variant":"LOLv2","rows":12,"metrics":["Average PSNR","LPIPS","SSIM"],"first_row_in_archive_order":{"model":"CFWD","paper":"/paper/low-light-image-enhancement-via-clip-fourier","metrics":{"Average PSNR":"29.855","SSIM":"0.891"},"code_links":[{"title":"hejh8/cfwd","url":"https://github.com/hejh8/cfwd"},{"title":"He-Jinhong/CFWD","url":"https://github.com/He-Jinhong/CFWD"}]},"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/expomamba-exploiting-frequency-ssm-blocks-for","title":"ExpoMamba: Exploiting Frequency SSM Blocks for Efficient and Effective Image Enhancement","date":"2024-08-19","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/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/low-light-image-enhancement-via-clip-fourier","title":"Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion","date":"2024-01-08","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/low-light-image-enhancement-with-wavelet","title":"Low-Light Image Enhancement with Wavelet-based Diffusion Models","date":"2023-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":12,"samples_unverified":1,"pointer_only_for_licence":0,"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/learning-semantic-aware-knowledge-guidance","title":"Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement","date":"2023-04-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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."}},{"paper":"/paper/low-light-image-enhancement-with-normalizing","title":"Low-Light Image Enhancement with Normalizing Flow","date":"2021-09-13","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":33,"samples_ran":18,"samples_unverified":15,"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."}