Datasets › DIV2K

DIV2K

Introduced in NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study1 Jan 2017 archive 2025-07-28

DIV2K is a popular single-image super-resolution dataset which contains 1,000 images with different scenes and is splitted to 800 for training, 100 for validation and 100 for testing. It was collected for NTIRE2017 and NTIRE2018 Super-Resolution Challenges in order to encourage research on image super-resolution with more realistic degradation. This dataset contains low resolution images with different types of degradations. Apart from the standard bicubic downsampling, several types of degradations are considered in synthesizing low resolution images for different tracks of the challenges. Track 2 of NTIRE 2017 contains low resolution images with unknown x4 downscaling. Track 2 and track 4 of NTIRE 2018 correspond to realistic mild ×4 and realistic wild ×4 adverse conditions, respectively. Low-resolution images under realistic mild x4 setting suffer from motion blur, Poisson noise and pixel shifting. Degradations under realistic wild x4 setting are further extended to be of different levels from image to image.

Source: Unsupervised Image Super-Resolution with an Indirect Supervised Path

Benchmarks archive 2025-07-28

All 3 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

Papers archive 2025-07-28

14 shown of 14 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 654. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise 2 1 5 Dec 2024 not harvested
Auto-Encoded Supervision for Perceptual Image Super-Resolution 1 1 28 Nov 2024 ran 2 of 6 samples (4 unverified; 6 pointer-only for licence)
Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration 1 1 5 May 2024 ran 11 of 12 samples (1 unverified; 11 pointer-only for licence)
Boosting Flow-based Generative Super-Resolution Models via Learned Prior 1 2 16 Mar 2024 not harvested
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge 1 1 16 Dec 2023 ran 0 of 2 samples (2 unverified)
Implicit Diffusion Models for Continuous Super-Resolution 1 7 29 Mar 2023 ran 5 of 10 samples (5 unverified; 10 pointer-only for licence)
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution 3 2 9 Mar 2023 ran 2 of 5 samples (3 unverified; 5 pointer-only for licence)
Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation 1 1 24 Nov 2022 not harvested
Flexible Style Image Super-Resolution using Conditional Objective 1 2 13 Jan 2022 not harvested
Learning Continuous Image Representation with Local Implicit Image Function 2 1 16 Dec 2020 not harvested
Multi-Step Reinforcement Learning for Single Image Super-Resolution 1 1 28 Jul 2020 not harvested
SRFlow: Learning the Super-Resolution Space with Normalizing Flow 8 1 25 Jun 2020 ran 2 of 3 samples (1 unverified; 3 pointer-only for licence)
Image Super-Resolution via Attention based Back Projection Networks 1 1 10 Oct 2019 not harvested
Enhanced Deep Residual Networks for Single Image Super-Resolution 45 1 10 Jul 2017 ran 4 of 4 samples (0 unverified; 2 pointer-only for licence)

Dataset loaders archive 2025-07-28

2 loaders as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Custom (research-only)

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • DIV2K
  • DIV2K val - 16x upscaling
  • DIV2K val - 2x upscaling
  • DIV2K val - 4x upscaling

4 variant names, as the archive lists them.

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