데이터셋 탐색

The LOL dataset is composed of 500 low-light and normal-light image pairs and divided into 485 training pairs and 15 testing pairs. The low-light images contain noise produced during the photo capture process. Most of the images are indoor scenes. All the images have a resolution of 400×600. Source: Unsupervised Real-world Low-light Image Enhancement with Decoupled Networks Image Source: https://daooshee.github.io/BMVC2018website/

LOL-v2-real contains 689 low-/normal-light image pairs for training and 100 pairs for testing.

The real captured dataset of LOL contains 500 low/normallight image pairs. Most low-light images are collected by changing exposure time and ISO, while other configurations of the cameras are fixed. We capture images from a variety of scenes, e.g., houses, campuses, clubs, streets. Since camera shaking, object movement, and lightness changing may cause misalignment between the image pairs, inspired by [41], a three-step shooting strategy is used to eliminate such misalignments between the image pairs in our dataset. For one scene, we first shoot two normal-light images $N_1$ and $N_2$. Then, we change the exposure time and ISO to capture a series of low-light images. Finally, we set the exposure time and ISO back to shoot another two normal-light images $N_3$ and $N_4$. The average of $N_i (i = 1,2,3,4)$ is treated as the ground-truth $G=\frac{1}{4}\sum^4_{i=1}N_i$. Then, we check whether there is object or camera movement. Specifically, the misalignment for these normal-light images is measured by $M=\frac{1}{4}\sum^4_{i=1}MSE(Ni, G)$. If M > 0.1, we abandon the corresponding pair. These raw images are resized to 400 × 600 and converted to Portable Network Graphics format. The dataset is publicly available.

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.)

From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement

To make synthetic images match the property of real dark photography, we analyze the illumination distribution of low-light images. We collect 270 low-light images from public MEF [42], NPE [6], LIME [8], DICM [43], VV,2 and Fusion [44] dataset, transform the imagesT into YCbCr channel and calculate the histogram of Y channel. We also collect 1000 raw images from RAISE [45] as normal-light images and calculate the histogram of Y channel in YCbCr. Raw images contain more information than the converted results. For raw images, all operations used to generate pixel values are performed in one step on the base data, making the result more accurate. 1000 raw images in RAISE [45] are used to synthesize low-light images. Interface provided by Adobe Lightroom is used and we try different kinds of parameters to make the histogram of Y channel fit the result in low-light images. Final parameter configuration can be found in the supplementary material. The illumination distribution of synthetic images matches that of low-light images. Finally, we resize these raw images to 400 × 600 and convert them to Portable Network Graphics format.

LoLi-Phone is a large-scale low-light image and video dataset for Low-light image enhancement (LLIE). The images and videos are taken by different mobile phones' cameras under diverse illumination conditions.

Despite the successes of recent developments in visual AI, different shortcomings still exist; from missing exact logical reasoning, to abstract generalization abilities, to understanding complex and noisy scenes. Unfortunately, existing benchmarks, were not designed to capture more than a few of these aspects. Whereas deep learning datasets focus on visually complex data but simple visual reasoning tasks, inductive logic datasets involve complex logical learning tasks, however, lack the visual component. To address this, we propose the visual logical learning dataset, V-LoL, that seamlessly combines visual and logical challenges. Notably, we introduce the first instantiation of V-LoL, V-LoL-Train, -- a visual rendition of a classic benchmark in symbolic AI, the Michalski train problem. By incorporating intricate visual scenes and flexible logical reasoning tasks within a versatile framework, V-LoL-Train provides a platform for investigating a wide range of visual logical learning challenges.

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!