Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement

By Ali Moghaddamjo Reza. J. VLSI Signal Process., 2004

1,655 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/efbb55c5a66d3d04307a80d1ca17113c48344c52

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ge Li
2. Xiaohui Yuan
3. Wencheng Wang
4. Zairui Gao
5. Zhenqiang Ying
6. Wen Gao
7. Xiaojin Wu
8. Wenhan Yang

In Media Technology

1. Ge Li
2. Xiaohui Yuan
3. Wencheng Wang
4. Zairui Gao
5. Zhenqiang Ying
6. Wen Gao
7. Xiaojin Wu
8. Wenhan Yang

Citations in the corpus

• A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement (2017, background): “Many extensions of HE are proposed to take some restrictions into account such as brightness preservation [29], [34], [10] and contrast limitation [35].”
• An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “To illustrate the performance of HE methods on color images, the AMHE [99], BBHE [73], CLAHE [82], DSIHE [74], HE [66], RMSHE [85], RSIHE [92], and WHE [100] algorithms are tested here in both the RGB and HSI color spaces.”