A Generalized Unsharp Masking Algorithm

By Guang-Yu Deng. IEEE Transactions on Image Processing, 2011

352 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/1e618a1f3db266c9c969e49e7be3ab42cdb2165d

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. Wenhan Yang
2. Xueyang Fu
3. Yue Huang
4. Jiaying Liu
5. Xinghao Ding
6. Jianmin Jiang
7. Pingping Zhang
8. Wen-Bin Wu

In Media Technology

1. Wenhan Yang
2. Xinghao Ding
3. Yue Huang
4. John William Paisley
5. Jianmin Jiang
6. Pingping Zhang
7. Wen-Bin Wu
8. Jian Weng

Citations in the corpus

• A fusion-based enhancing method for weakly illuminated images (2016, methodology): “In this experiment, we use 40 different kinds of weakly illumination images and enhance them using [1,10,16,20,22,23] and our proposed method.”
• Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior (2016, neutral): “Traditional image enhancement methods (e.g., Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE) [6], Generalized Unsharp Masking (GUM) [7], and Probability-based method (PB) [8]) are effective for common images.”
• URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement (2022, methodology): “Especially for the low-light image enhancement (LLIE), many methods have been proposed, including histogram equalization [29], unsharp masking algorithms [7], Retinex-based methods [12, 27], multiple exposure fusion [5], and deep learning-based methods [11, 34].”