Image Deblurring

The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided. * Total number of non-empty WordNet synsets: 21841 * Total number of images: 14197122 * Number of images with bounding box annotations: 1,034,908 * Number of synsets with SIFT features: 1000 * Number of images with SIFT features: 1.2 million Source: ImageNet Large Scale Visual Recognition Challenge Image Source: https://cs.stanford.edu/people/karpathy/cnnembed/

CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age. Source: Show, Attend and Translate: Unpaired Multi-Domain Image-to-Image Translation with Visual Attention Image Source: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

The GoPro dataset for deblurring consists of 3,214 blurred images with the size of 1,280×720 that are divided into 2,103 training images and 1,111 test images. The dataset consists of pairs of a realistic blurry image and the corresponding ground truth sharp image that are obtained by a high-speed camera. Source: Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networksfor Non-Uniform Single Image Deblurring Image Source: Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring

The dataset consists of 4,738 pairs of images of 232 different scenes including reference pairs. All images were captured both in the camera raw and JPEG formats, hence generating two datasets: RealBlur-R from the raw images, and RealBlur-J from the JPEG images. Each training set consists of 3,758 image pairs, while each test set consists of 980 image pairs. The deblurring result is first aligned to its ground truth sharp image using a homography estimated by the enhanced correlation coefficients method, and PSNR or SSIM is computed in sRGB color space.

Consists of 8,422 blurry and sharp image pairs with 65,784 densely annotated FG human bounding boxes. Source: Human-Aware Motion Deblurring

A total of 170 videos for training and 30 videos for testing, each of which has 60 frames, amounting to 12,000 paired data. (Note that the first and last 30 frames of each video are NOT consecutive, and their darknesses are simulated differently as well.)

This dataset includes sharp-blur pairs of Leishmania image, which is a protozoan parasite microscopy image dataset of Leishmania, obtained from the preserved slides stained with Giemsa. The paired blur-sharp images are acquired by employing a bright-field microscope (Olympus IX53) with 100× magnification oil immersion objectives.We first capture the sharp images as ground truth, then acquire its corresponding out-of-focus images. The extent and nature of defocusing are random along the optical axis, where the degree of out-of-focus is inconsistent from image-to-image. This dataset includes 764 in-focus and 764 corresponding out-of-focus images, where each image is composed of 2304 × 1728 pixels in 24-bit JPG format.

We introduce low-light image enhancement benchmark dataset “Low-light Images of Streets (LoLI-Street),” which contains three subsets: train, validation, and test. The train and validation sets consist of 30k and 3k paired low-light and high-light images, respectively, and the real low-light test set (RLLT) contains 1k images under real-world low-light conditions, totaling 33k images. 1. Download LoLI-Street Dataset 2. Read Full Paper ## Citation: If you use the dataset, please cite our paper: python @InProceedings{Islam_2024_ACCV, author = {Islam, Md Tanvir and Alam, Inzamamul and Woo, Simon S. and Anwar, Saeed and Lee, IK Hyun and Muhammad, Khan}, title = {LoLI-Street: Benchmarking Low-light Image Enhancement and Beyond}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {December}, year = {2024}, pages = {1250-1267}} Thank you!