yunhengzi/image-processing-datasets

Image Processing Datasets

A curated list of image processing datasets in regions of brightening, HDR, color enhancement and inpainting.

The list is maintained by Wenjing Wang, Dejia Xu, Qingyang Li, Wenhan Yang from STRUCT Group at PKU.

Brightening

• VIP-LowLight Dataset [WEB]
  • Eight Natural Images Captured in Very Low-Light Conditions, Audrey Chung.
• ReNOIR [PDF] [WEB]
  • RENOIR - A Dataset for Real Low-Light Image Noise Reduction (JVCIR2018), Josue Anaya, Adrian Barbu
• Raw Image Low-Light Object Dataset [WEB]
  • Dan Richards, James Sergeant, Michael Milford, Peter Corke
• Learning to See in the Dark [PDF] [WEB]
  • Learning to See in the Dark (CVPR2018), Chen Chen, Qifeng Chen, Jia Xu, Vladlen Koltun.
• ExDARK [PDF]
  • Getting to Know Low-light Images with The Exclusively Dark Dataset (Submitted to CVIU), Yuen Peng Loh, Chee Seng Chan.

Color-Enhancement

• MIT FiveK dataset [PDF] [WEB]
  • Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs (CVPR2011), Vladimir Bychkovsky, Sylvain Paris and Eric Chan, Fredo Durand.
• LRAICE-Dataset [PDF] [WEB]
  • A Learning-to-Rank Approach for Image Color Enhancement (CVPR2014), Jianzhou Yan, Stephen Lin, Sing Bing Kang, Xiaoou Tang.
• DPED dataset [PDF] [WEB]
  • DSLR-quality photos on mobile devices with deep convolutional networks (ICCV2017), A. Ignatov, N. Kobyshev, K. Vanhoey, R. Timofte, L. Van Gool.
• The 500px Dataset [PDF]
  • Exposure: A White-Box Photo Post-Processing Framework (TOG2018), Yuanming Hu, Hao He, Chenxi Xu, Baoyuan Wang, Stephen Lin.

Image-Inpainting

• Image Inpainting [WEB]
  • 2018 Chalearn Looking at People Satellite Workshop ECCV

Image Denoising

• Smartphone Image Denoising Dataset [PDF]
  • A High-Quality Denoising Dataset for Smartphone Cameras (CVPR2018), Abdelrahman Abdelhamed, Stephen Lin, Michael S. Brown.
• Darmstadt Noise Dataset [PDF] [WEB]
  • Benchmarking Denoising Algorithms with Real Photographs (CVPR2017), Tobias Plötz and Stefan Roth.
• PolyU Dataset [PDF] [WEB]
  • Real-world Noisy Image Denoising: A New Benchmark (Arxiv2017), Jun Xu, Hui Li, Zhetong Liang, David Zhang, and Lei Zhang.
• RENOIR Dataset [PDF] [WEB]
  • A Dataset for Real Low-Light Image Noise Reduction (Arxiv2014), J. Anaya, A. Barbu.
• Holistic Dataset [PDF] [WEB]
  • A Holistic Approach to Cross-Channel Image Noise Modeling and its Application to Image Denoising (CVPR2016), Seonghyeon Nam, Youngbae Hwang, Yasuyuki Matsushita, Seon Joo Kim.

Super-Resolution and Up-Sampling

• Train91 [PDF] [WEB]
  • Image Super-Resolution via Sparse Representation (TIP2010), Jianchao Yang, John Wright, Thomas Huang, and Yi Ma.
• Set5 [PDF] [WEB]
  • Anchored Neighborhood Regression for Fast Example-Based Super-Resolution (ICCV2013), Radu Timofte, Vincent De Smet, and Luc Van Gool.
• Set14 [PDF]
  • On Single Image Scale-Up Using Sparse-Representations (International conference on curves and surfaces 2010), Zeyde, Roman and Elad, Michael and Protter, Matan.
• B100 [PDF] [WEB]
  • Contour Detection and Hierarchical Image Segmentation (TPAMI2011), P. Arbelaez, M. Maire, C. Fowlkes and J. Malik.
• Urban100 [PDF] [WEB]
  • Single Image Super-Resolution from Transformed Self-Exemplars (CVPR2015), Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja.
• DIV2K [PDF] [WEB]
  • NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study (CVPRW2017), Eirikur Agustsson, Radu Timofte.
• LIVE [PDF] [WEB]
  • A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms (TIP2006), H.R. Sheikh, M.F. Sabir and A.C. Bovik.
• Super-Resolution Erlangen (SupER) [PDF] [WEB]
  • Benchmarking Super-Resolution Algorithms on Real Data (Arxiv2017), Thomas Köhler, Michel Bätz, Farzad Naderi, André Kaup, Andreas Maier, and Christian Riess.

Dehazing

• Waterloo IVC Dehazed Image Database [PDF] [WEB]
  • Perceptual evaluation of single image dehazing algorithms (ICIP'15), Kede Ma, Wentao Liu and Zhou Wang.
• FRIDA dataset [WEB]
• D-hazy [PDF] [WEB]
• CHIC [PDF]
  • A Color Image Database for Haze Model and Dehazing Methods Evaluation
• HazeRD [PDF]
  • HazeRD: an outdoor dataset for dehazing algorithms
• I-HAZE : a dehazing benchmark with real hazy and haze-free outdoor images [PDF]
• O-HAZE : a dehazing benchmark with real hazy and haze-free outdoor images [PDF]
• RESIDE: A Benchmark for Single Image Dehazing [WEB]

Deblurring (sharpening)

• Understanding and evaluating blind deconvolution algorithms (CVPR'09) [PDF]
• Edge-based blur kernel estimation using patch priors [PDF]
• Benchmarking blind deconvolution with a real-world database (ECCV'12) [PDF]
• A Comparative Study for Single Image Blind Deblurring (CVPR'16) [WEB]

De-rain

rain streak removal

• Rain Streak Database [PDF] [WEB]
  • Photorealistic rendering of rain streaks
• Single Image Rain Streak Decomposition Using Layer Priors [PDF] [DATASET]
• Rain100L, Rain20L and Rain100H [WEB]
  • Deep Joint Rain Detection and Removal From a Single Image
• MS-CSC-Rain-Streak-Removal [WEB]
  • Video Rain Streak Removal By Multiscale ConvolutionalSparse Coding
• DID-MDN [WEB]
  • Density-aware Single Image De-raining using a Multi-stream Dense Network

rain drop removal

• Attentive Generative Adversarial Network for Raindrop Removal from A Single Image (CVPR'18) [WEB]