{"url":"/dataset/lol","name":"LOL","full_name":"LOw-Light dataset","description_markdown":"The **LOL** dataset is composed of 500 low-light and normal-light image pairs and divided into 485 training pairs and 15 testing pairs. The low-light images contain noise produced during the photo capture process. Most of the images are indoor scenes. All the images have a resolution of 400×600.\r\n\r\nSource: [Unsupervised Real-world Low-light Image Enhancement with Decoupled Networks](https://arxiv.org/abs/2005.02818)\r\nImage Source: [https://daooshee.github.io/BMVC2018website/](https://daooshee.github.io/BMVC2018website/)","description_withheld":null,"homepage":"https://daooshee.github.io/BMVC2018website/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/deep-retinex-decomposition-for-low-light","title":"Deep Retinex Decomposition for Low-Light Enhancement","first_author":"Chen Wei","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Low-Light Image Enhancement","url":"/task/low-light-image-enhancement","datasets_with_task":"/datasets/task/low-light-image-enhancement"},{"name":"Unified Image Restoration","url":"/task/unified-image-restoration","datasets_with_task":"/datasets/task/unified-image-restoration"}],"languages":[],"variants":["LOL"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/lol-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":257,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/low-light-image-enhancement-on-lol","task":"Low-Light Image Enhancement","dataset_variant":"LOL","rows":40,"metrics":["Average PSNR","LPIPS","SSIM","FLOPS (G)","Params (M)","SSIM (sRGB)","Number of params","BSQ-rate over MS-SSIM","FID"],"first_row_in_archive_order":{"model":"CFWD","paper":"/paper/low-light-image-enhancement-via-clip-fourier","metrics":{"Average PSNR":"29.185","SSIM":"0.872"},"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"},{"leaderboard":"/sota/unified-image-restoration-on-lol","task":"Unified Image Restoration","dataset_variant":"LOL","rows":1,"metrics":["Average PSNR (dB)"],"first_row_in_archive_order":{"model":"DA-RCOT","paper":"/paper/degradation-aware-residual-conditioned","metrics":{"Average PSNR (dB)":"23.25"},"code_links":[{"title":"xl-tang3/RCOT","url":"https://github.com/xl-tang3/RCOT"},{"title":"xl-tang3/DA-RCOT","url":"https://github.com/xl-tang3/DA-RCOT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/forward-only-diffusion-probabilistic-models","title":"Forward-only Diffusion Probabilistic Models","date":"2025-05-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/degradation-aware-residual-conditioned","title":"Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration","date":"2024-11-03","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":6,"samples_unverified":0,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bayesian-enhancement-models-for-one-to-many","title":"Bayesian Enhancement Models for One-to-Many Mapping in Image Enhancement","date":"2024-10-13","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/image-enhancement-based-on-histogram-guided","title":"Image Enhancement Based on Histogram-Guided Multiple Transformation Function Estimation","date":"2024-10-08","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/onerestore-a-universal-restoration-framework","title":"OneRestore: A Universal Restoration Framework for Composite Degradation","date":"2024-07-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":7,"samples_unverified":6,"pointer_only_for_licence":13,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":2,"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/ppformer-using-pixel-wise-and-patch-wise","title":"PPformer: Using pixel-wise and patch-wise cross-attention for low-light image enhancement","date":"2024-01-15","rows_on_this_dataset":1,"code_links":1,"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/resfusion-prior-residual-noise-embedded","title":"Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise","date":"2023-11-25","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/wavenet-wave-aware-image-enhancement","title":"WaveNet: Wave-Aware Image Enhancement","date":"2023-10-10","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/controlling-vision-language-models-for","title":"Controlling Vision-Language Models for Multi-Task Image Restoration","date":"2023-10-02","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":8,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multiple-transformation-function-estimation","title":"Multiple transformation function estimation for image enhancement","date":"2023-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cdan-convolutional-dense-attention-guided","title":"CDAN: Convolutional dense attention-guided network for low-light image enhancement","date":"2023-08-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/glow-in-the-dark-low-light-image-enhancement","title":"Glow in the Dark: Low-Light Image Enhancement with External Memory","date":"2023-07-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/pyramid-diffusion-models-for-low-light-image","title":"Pyramid Diffusion Models For Low-light Image Enhancement","date":"2023-05-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":7,"samples_unverified":1,"pointer_only_for_licence":8,"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":2,"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/unsupervised-night-image-enhancement-when","title":"Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression","date":"2022-07-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/0-1-deep-neural-networks-via-block-coordinate","title":"0/1 Deep Neural Networks via Block Coordinate Descent","date":"2022-06-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/illumination-adaptive-transformer","title":"You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction","date":"2022-05-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/treenhance-an-automatic-tree-search-based","title":"TreEnhance: A Tree Search Method For Low-Light Image Enhancement","date":"2022-05-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/half-wavelet-attention-on-m-net-for-low-light","title":"Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement","date":"2022-03-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/maxim-multi-axis-mlp-for-image-processing","title":"MAXIM: Multi-Axis MLP for Image Processing","date":"2022-01-09","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":46,"samples_ran":27,"samples_unverified":19,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-low-light-image-enhancement-via","title":"Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior","date":"2021-12-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/low-light-image-enhancement-via-breaking-down","title":"Low-light Image Enhancement via Breaking Down the Darkness","date":"2021-11-30","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":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},{"paper":"/paper/190504161","title":"Kindling the Darkness: A Practical Low-light Image Enhancer","date":"2019-05-04","rows_on_this_dataset":1,"code_links":4,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":11,"samples_harvested":114,"samples_ran":77,"samples_unverified":37,"pointer_only_for_licence":33,"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."}