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Blind Super-Resolution datasets

archive 2025-07-28

9 datasets carry the task tag "Blind Super-Resolution" (the task itself: Blind Super-Resolution), ordered by the archive's paper count. Page 1 of 1: 9 shown of 9. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

All 12,172 Question Answering 413Semantic Segmentation 347Object Detection 332Image Classification 283Classification 222Text Classification 167Language Modelling 166Text Generation 162Visual Question Answering (VQA) 144Named Entity Recognition (NER) 130Pose Estimation 124Anomaly Detection 119Action Recognition 115Instance Segmentation 111Sentiment Analysis 104Text Summarization 98Domain Adaptation 96Speech Recognition 96Reading Comprehension 95Information Retrieval 932D Object Detection 92Image Retrieval 87Machine Translation 82Relation Extraction 822D Semantic Segmentation 81Natural Language Inference 81Image Captioning 79Image Generation 79Depth Estimation 77Node Classification 75Natural Language Understanding 72Autonomous Driving 70Code Generation 70Object Tracking 693D Object Detection 67Face Recognition 67Link Prediction 67Person Re-Identification 65Data Augmentation 63Multi-Task Learning 593D Reconstruction 58Common Sense Reasoning 56Recommendation Systems 56Video Understanding 56Optical Character Recognition (OCR) 55Graph Classification 54Abstractive Text Summarization 52Emotion Recognition 52Medical Image Segmentation 52Word Embeddings 52Blind Super-Resolution 9

Blind Super-Resolution datasets 1–9 of 9

BSD (Berkeley Segmentation Dataset)
BSD is a dataset used frequently for image denoising and super-resolution.
ImagesImage Super-ResolutionColor Image DenoisingGrayscale Image DenoisingImage DenoisingBlind Super-ResolutionDensity Estimation+3 tasks
718 papers · 48 benchmarks
Urban100
The Urban100 dataset contains 100 images of urban scenes.
ImagesImage Super-ResolutionColor Image DenoisingGrayscale Image DenoisingImage DenoisingBlind Super-ResolutionJoint Demosaicing and Denoising+1 tasks
591 papers · 25 benchmarks
Set14
The Set14 dataset is a dataset consisting of 14 images commonly used for testing performance of Image Super-Resolution models.
ImagesImage Super-ResolutionBlind Super-Resolution
532 papers · 8 benchmarks
Set5
The Set5 dataset is a dataset consisting of 5 images (“baby”, “bird”, “butterfly”, “head”, “woman”) commonly used for testing performance of Image Super-Resolution models.
ImagesImage Super-ResolutionBlind Super-ResolutionImage RescalingCompressive Sensing
444 papers · 9 benchmarks
Manga109
Manga109 has been compiled by the Aizawa Yamasaki Matsui Laboratory, Department of Information and Communication Engineering, the Graduate School of Information Science and Technology, the University of Tokyo.
ImagesObject DetectionImage Super-ResolutionBlind Super-ResolutionFace DetectionBody Detection
300 papers · 12 benchmarks
BSD100
Image Super-ResolutionBlind Super-ResolutionJoint Demosaicing and Denoising
80 papers · 0 benchmarks
DRealSR (Diverse Real-world image Super-Resolution)
DRealSR establishes a Super Resolution (SR) benchmark with diverse real-world degradation processes, mitigating the limitations of conventional simulated image degradation.
ImagesSuper-ResolutionImage Super-ResolutionBlind Super-ResolutionSSIM
36 papers · 1 benchmark
DIV2KRK (DIV2K Random Kernel)
Using the validation set (100 images) from the widely used DIV2K dataset, we blurred and subsampled each image with a different, randomly generated kernel.
Blind Super-Resolution
26 papers · 2 benchmarks
KID-F (K-pop Idol Dataset - Female)
Description K-pop Idol Dataset - Female (KID-F) is the first dataset of K-pop idol high quality face images.
ImagesSuper-ResolutionBlind Super-ResolutionFace Hallucination
0 papers · 0 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.