Abstract Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light imagery have seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as a benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2 of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought. It consists exclusively of low-light images captured in visible light only, with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing the visualizations of both hand-crafted and learned features. We found that the effects of low-light reach far deeper into the features than can be solved by simple “illumination invariance”. It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The dataset can be downloaded at https: github.com cs-chan Exclusively-Dark-Image-Dataset .
At nighttime, reduced visibility could cause foreground and background images to appear to blend together. However, ambient light is always present in the natural environment, and as a consequence, it creates some contrast in darkness. In this paper, we formulate a visual analytic method that automatically unveils the contrast of dark images (i.e. nighttime), revealing the "hidden" contents. We utilize the traits of image representations obtained from computer vision techniques through a learning based inversion algorithm, eliminating the reliance on night vision camera and at the same time minimizing the need for human intervention (i.e. manual fine-tuning the gamma correction using Adobe Photoshop software). Experiments using the new Malaya Pedestrian in the Dark (MyPD) dataset that we have collected from the website Flickr, and in comparison with conventional methods such as image integral and gamma correction, it shows the efficacy of the proposed method. Additionally, we show the potential of this framework in applications that could benefit public safety.