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LIME: A Method for Low-light IMage Enhancement

About

When one captures images in low-light conditions, the images often suffer from low visibility. This poor quality may significantly degrade the performance of many computer vision and multimedia algorithms that are primarily designed for high-quality inputs. In this paper, we propose a very simple and effective method, named as LIME, to enhance low-light images. More concretely, the illumination of each pixel is first estimated individually by finding the maximum value in R, G and B channels. Further, we refine the initial illumination map by imposing a structure prior on it, as the final illumination map. Having the well-constructed illumination map, the enhancement can be achieved accordingly. Experiments on a number of challenging real-world low-light images are present to reveal the efficacy of our LIME and show its superiority over several state-of-the-arts.

Xiaojie Guo• 2016

Related benchmarks

TaskDatasetResultRank
Human Pose EstimationExLPose-OCN (test)
AP@0.5:0.95 (A7M3)33.2
23
Human Pose EstimationExLPose Low-light-normal (LL-N)
AP (IoU 0.5:0.95)31.9
22
Pose EstimationExLPose High low-light LL-H (test)
AP@0.5:0.9521.2
7
Pose EstimationExLPose LL-E Extreme low-light (test)
AP@0.5:0.957.6
7
Pose EstimationExLPose Average low-light LL-A (test)
AP (0.5:0.95)21.1
7
Pose EstimationExLPose Well-lit (test)
AP@0.5:0.9557.7
7
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