Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement

By Haidi Ibrahim, Nicholas Sia Pik Kong. IEEE transactions on consumer electronics, 2007

1,023 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/50b714de285fa093acb30efe52086dc8ac4896d2

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. Chongyi Li
2. Chunle Guo
3. Wenhan Yang
4. Jiaying Liu
5. Chen Change Loy
6. Feng Lu
7. Feifan Lv
8. Yu Li

Citations in the corpus

• An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, background): “…subhistogram equalization (EDSHE) [93], dynamic histogram equalization (DHE) [94], brightness-preserving dynamic histogram equalization (BPDHE) [95], bi-histogram equalization with a plateau limit (BHEPL) [96], median-mean-based subimage-clipped histogram equalization (MMSICHE) [97],…”
• Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement (2020, background): “Histogram distribution of images is adjusted at both global [6, 9] and local levels [23, 13].”
• Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, background): “37 BPDHE [35] 13.”
• MBLLEN: Low-Light Image/Video Enhancement Using CNNs (2018, background): “In particular, BPDHE [15] tries to preserve image brightness dynamically; Arici et al .”
• From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (2020, background): “Metric BIMEF [31] BPDHE [10] CRM [33] DHECE [22] Dong [6] EFF [32] CLAHE [36] LIME [9] MF [7]”
• Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation (2021, background): “Histogram distribution of images is adjusted at both global [12], [13] and local levels [14], [15].”
• A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement (2017, background): “In general, image enhancement techniques can improve the subjective visual quality of input images and support the extraction of valuable information for some computer vision techniques [29], [16].”
• Brightness preserving dynamic fuzzy histogram equalization (2010, methodology): “Here we introduce a modification to BPDHE [5] technique with the use of fuzzy statistics of digital images (fuzzy histogram) [7].”