SSIM

The I-Haze dataset contains 25 indoor hazy images (size 2833×4657 pixels) training. It has 5 hazy images for validation along with their corresponding ground truth images. Source: Single image dehazing for a variety of haze scenarios using back projected pyramid network Image Source: https://data.vision.ee.ethz.ch/cvl/ntire18//i-haze/

DRealSR establishes a Super Resolution (SR) benchmark with diverse real-world degradation processes, mitigating the limitations of conventional simulated image degradation. It has been collected from five DSLR cameras in natural scenes and cover indoor and outdoor scenes avoiding moving objects, e.g., advertising posters, plants, offices, buildings. The training images are cropped into 380×380, 272×272 and 192×192 patches, resulting in 31,970 patches. Source: Component Divide-and-Conquer for Real-World Image Super-Resolution

Various documents dataset. Each of the 65 documents includes scanned ground truth images, both hard and easy distorted photos, and document-centered cropped images. Image Source: DocUNet: Document Image Unwarping via a Stacked U-Net

VIDIT is a reference evaluation benchmark and to push forward the development of illumination manipulation methods. VIDIT includes 390 different Unreal Engine scenes, each captured with 40 illumination settings, resulting in 15,600 images. The illumination settings are all the combinations of 5 color temperatures (2500K, 3500K, 4500K, 5500K and 6500K) and 8 light directions (N, NE, E, SE, S, SW, W, NW). Original image resolution is 1024x1024. Source: VIDIT: Virtual Image Dataset for Illumination Transfer Image source: https://github.com/majedelhelou/VIDIT