Brightness preserving histogram equalization with maximum entropy: a variational perspective
By Chao Wang, Zhongfu Ye. IEEE transactions on consumer electronics, 2005
438 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/1416d8c9af07269016de2d49c24b6495a07d4385
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In Computer Vision and Pattern Recognition
- Ge Li
- Jyotirmoy Chatterjee
- Shih-Chia Huang
- Laurent Najman
- Amit Suveer
- Yi-Sheng Chiu
- Arnaldo de Albuquerque Araújo
- Manjunatha Mahadevappa
Citations in the corpus
- Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution (2013, methodology): “The Brightness-Preserving Histogram Equalization with Maximum Entropy (BPHEME) method preserves the brightness and also maximizes the entropy of the enhanced image via histogram speciation [10].”
- Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving (2007, methodology): “Another method, which will not be described in Section III but is of relevance for this work, is the variational framework based on histogram transformation for image contrast enhancement and brightness preserving with maximum entropy (BPHEME) presented in [2].”
- Brightness preserving dynamic fuzzy histogram equalization (2010, methodology): “To overcome this limitation, several brightness preserving histogram modification approaches, such as bi-histogram equalization (BBHE [2], MMBEBHE [3]), multi-histogram equalization (DHE [4], BPDHE [5]) and histogram specification (BPHEME [6]) have been proposed in literature.”
- Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement (2007, background): “In this condition, the output image is exactly the copy of the input image, and there is no enhancement at all [7].”
- Bi-histogram equalization with a plateau limit for digital image enhancement (2009, background): “Furthermore, bisections MBPHE can only preserve the original mean brightness if and only if the input histogram has a quasi-symmetrical distribution around its separating point [10].”
- 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].”