Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution

By Shih-Chia Huang, Fan-Chieh Cheng, Yi-Sheng Chiu. IEEE Transactions on Image Processing, 2013

1,108 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/787cc6d361dd10459603d1a77f99347eaa5ca8ff

How researchers see this paper

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

In Atomic and Molecular Physics, and Optics

1. Dacheng Tao
2. Guangtao Zhai
3. Ke Gu
4. Weisi Lin
5. J. Qiao
6. Yonghua Zhang
7. Min Liu
8. Jianrui Cai

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Jiayi Ma
3. Luc Van Gool
4. Lei Zhang
5. Xiaojie Guo
6. Ke Gu
7. Radu Timofte
8. Weisi Lin

Citations in the corpus

• Beyond Brightening Low-light Images (2021, background): “Several adaptive gamma correction (AGC) algorithms have been proposed to relieve this problem (Huang et al. 2013; Rahman et al. 2016; Wang et al. 2009).”
• Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images (2018, methodology): “The codes of [8], [3], [61], [1] are from the original authors, and [5], [6] are from a contrast enhancement toolbox [62].”
• Getting to Know Low-light Images with The Exclusively Dark Dataset (2018, background): “Even in low-light image enhancement works, real low-light images were mostly downloaded or captured on an ad hoc basis ([15], [16], [17], [18]).”
• Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement (2020, methodology): “In the past decades, classical LIE methods often perform histogram equalization [28, 4, 43] or gamma correction [11, 29, 35] to enhance low-light images.”
• Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (2018, methodology): “Then the proposed blind BIQME algorithm is used to optimize these three parameters: where Q B , T R and T A are respectively associated to BIQME, RICE and AGCWD.”
• The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement (2016, methodology): “[40] presented a simple transformation model via the adaptive gamma correction with weighting distribution (AGCWD).”
• An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “In [59], an HE method with gamma correction was proposed and achieved a balance between the quality of the output image and the computing time.”