computer-vision-2
Image Enhancement
3260 papers • 126 benchmarks • 313 datasets
Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer. Source: A Comprehensive Review of Image Enhancement Techniques
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in image-enhancement-2
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Dataset
Best Model
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MIT-Adobe 5k
MIT-Adobe 5k
TIP 2018
TIP 2018
Exposure-Errors
Exposure-Errors
Libraries
iUse these libraries to find image-enhancement-2 models and implementations
2 papers 429
Datasets
Subtasks
Most implemented papers
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Chen Change Loy, Runmin Cong, Chongyi Li, S. Kwong, Chunle Guo, Junhui Hou, Jichang Guo•Sat Jan 18 2020
A novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network and shows that it generalizes well to diverse lighting conditions.
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