To evaluate the performance on 4K burst images/video, we collect several clips from website. The dataset can be download from : https://drive.google.com/file/d/1YDljUONvyKUO24smTx__CUH_4Zxhle09/view?usp=sharing

Description K-pop Idol Dataset - Female (KID-F) is the first dataset of K-pop idol high quality face images. It consists of about 6,000 high quality face images at 512x512 resolution and identity labels for each image. We collected about 90,000 K-pop female idol images and crop the face from each image. And we classified high quality face images. As a result, there are about 6,000 high quality face images in this dataset. There are 300 test datasets for a benchmark. There are no duplicate images between test and train images. Some identities in test images are not duplicated with train images. (It means some test images is new identity to the trained model) Each test images have its degraded pair. You can use these degraded test images for testing face super resolution performance. We also provide identity labels for each image. You can download the csv file from our github # Download You can download dataset from here. Google Drive # Agreement - The use of this software is RESTRICTED to non-commercial research and educational purposes. - All images of the KID-F dataset are obtained from the internet which are not property of EDA(PCEO-AI-CLUB). EDA is not responsible for the content nor the meaning of these images. - You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data. - You agree not to further copy, publish or distribute any portion of the KID-F dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset. - EDA reserves the right to terminate your access to the CelebA dataset at any time.

A real-world dataset for multi-image super-resolution that matches low-resolution Sentinel-2 images with high-resolution WorldView-2 images. The dataset has been introduced with the paper: - Kowaleczko, P., Tarasiewicz, T., Ziaja, M., Kostrzewa, D., Nalepa, J., Rokita, P., & Kawulok, M. (2023). A real-world benchmark for Sentinel-2 multi-image super-resolution. Scientific Data, 10(1), 644. The dataset can be downloaded from: https://doi.org/10.7910/DVN/1JMRAT

We propose a new light field image database called “PINet” inheriting the hierarchical structure from WordNet. It consists of 7549 LIs captured by Lytro Illum, which is much larger than the existing databases. The images are manually annotated to 178 categories according to WordNet, such as cat, camel, bottle, fans, etc. The registered depth maps are also provided. Each image is generated by processing the raw LI from the camera by Light Field Toolbox v0.4 for demosaicing and devignetting. PINet is the largest light field image dataset. More details can be found in https://github.com/VincentChandelier/SADN