JPEG Artifact Correction

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

Berkeley Segmentation Data Set 500 (BSDS500) is a standard benchmark for contour detection. This dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries. It includes 500 natural images with carefully annotated boundaries collected from multiple users. The dataset is divided into three parts: 200 for training, 100 for validation and the rest 200 for test. Source: Object Contour Detection with a Fully Convolutional Encoder-Decoder Network Image Source: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html

The LIVE Public-Domain Subjective Image Quality Database is a resource developed by the Laboratory for Image and Video Engineering at the University of Texas at Austin. It contains a set of images and videos whose quality has been ranked by human subjects. This database is used in Quality Assessment (QA) research, which aims to make quality predictions that align with the subjective opinions of human observers. The database was created through an extensive experiment conducted in collaboration with the Department of Psychology at the University of Texas at Austin. The experiment involved obtaining scores from human subjects for many images distorted with different distortion types. The QA algorithm may be trained on part of this data set, and tested on the rest. The database is available to the research community free of charge. If you use these images in your research, the creators kindly ask that you reference their website and their papers. There are two releases of the database. Release 2 includes more distortion types and more subjects than Release 1. The distortions include JPEG-compressed images, JPEG2000-compressed images, Gaussian blur, and white noise.

Five classic grayscale images commonly used for image quality assessment tasks.

A carefully chosen set of high-resolution high-precision natural images suited for compression algorithm evaluation. The images historically used for compression research (lena, barbra, pepper etc...) have outlived their useful life and its about time they become a part of history only. They are too small, come from data sources too old and are available in only 8-bit precision. These high-resolution high-precision images have been carefully selected to aid in image compression research and algorithm evaluation. These are photographic images chosen to come from a wide variety of sources and each one picked to stress different aspects of algorithms. Images are available in 8-bit, 16-bit and 16-bit linear variations, RGB and gray. These Images are available without any prohibitive copyright restrictions. These images are (c) there respective owners. You are granted full redistribution and publication rights on these images provided: 1. The origin of the pictures must not be misrepresented; you must not claim that you took the original pictures. If you use, publish or redistribute them, an acknowledgment would be appreciated but is not required. 2. Altered versions must be plainly marked as such, and must not be misinterpreted as being the originals. 3. No payment is required for distribution of this material, it must be available freely under the conditions stated here. That is, it is prohibited to sell the material. 4. This notice may not be removed or altered from any distribution. For grayscale evaluation, use the Grayscale 8 bit dataset, for color evaluation, use the Color 8 bit dataset. @online{icb, author = {Rawzor}, title = {Image Compression Benchmark}, url = {http://imagecompression.info/} }