Low Light Dataset

Introduced by Khan. et al. Divide and conquer: Ill-light image enhancement via hybrid deep network https://www.sciencedirect.com/science/article/abs/pii/S0957417421004759

The images captured under ill-lighting conditions, such as above lit, low-lit, back-lit, front-lit, and combination of them, with the robust exposure variation, suffer from various artefacts due to non-uniform lighting conditions. DILCOD consisting of 1000 training images (500-low light and 500 normal light) and 38 test images with respective ground truth images to evaluate the performance of our network. The captured test images are dividedequally into test-data-samples-A and data-samples B with corresponding ground-truth images. Images in test-data-sample-A are captured with mainly front diverse exposure variation using low light in the background. In contrast, theimages in the test-data-sample-B consist of back-lit images in which the background contains a point light source, but the front region is almost under-exposed. We use diverse lighting conditions to capture the scenes by using (CREVIS MV-CS30G with 8 mm lens) cameras in our capturing system. We utilized state of the art multi-lighting system to fulfil this challenging task. The camera’s baseline varies at 30 cm, with each camera at 30 degree. The images are captured using point light sources, and ground truth images are captured with a sufficiently large light with the best available visual appearance. You are free to use train-test pairs __ GTs__, and make new combinations! Cheers!!!