Data Collection
The LIS dataset comprises paired images collected across various scenes, encompassing both indoor and outdoor environments. To ensure a comprehensive range of low-light conditions, we utilized different ISO levels (e.g., 800, 1600, 3200, 6400) for long-exposure reference images and deliberately adjusted exposure times using varying low-light factors (e.g., 10, 20, 30, 40, 50, 100) to simulate extremely low-light conditions accurately. Each image pair in the LIS dataset includes instances of common object classes (bicycle, car, motorcycle, bus, bottle, chair, dining table, TV), accompanied by precise instance-level pixel-wise labels. These annotations serve as essential metrics for evaluating the performance of proposed methods in terms of object detection and instance segmentation.
Dataset characteristics
Paired samples
In the LIS dataset, we provide images in both sRGB-JPEG (typical camera output) and RAW formats, each format consists of paired short-exposure low-light and corresponding long-exposure normal-light images. We term these four types of images as sRGBdark, sRGB-normal, RAW-dark, and RAW-normal.To ensure they are pixel-wise aligned, we mount the camera on a sturdy tripod and avoid vibrations by remote control via a mobile app.
Diverse scenes
The LIS dataset consists of 2230 image pairs, which are collected in various scenes, including indoor and outdoor. To increase the diversity of low-light conditions, we use a series of ISO levels (e.g., 800, 1600, 3200, 6400) to take long-exposure reference images, and we deliberately decrease the exposure time by a series of low-light factors (e.g., 10, 20, 30, 40, 50, 100) to take short-exposure images for simulating very low-light conditions.
Instance-level pixel-wise labels
For each pair of images, we provide precise instance-level pixel-wise labels annota instances of 8 most common object classes in our daily life (bicycle, car, motorcycle, bus, bottle, chair, dining table, tv).