Deep Learning Strategies for Addressing Anomalous Exposure in Image Processing: The FARDBUNet Approach-Reference-Cited by-同舟云学术

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Deep Learning Strategies for Addressing Anomalous Exposure in Image Processing: The FARDBUNet Approach

Published:2023-12-06 Issue: Volume:31 Page:39-46
ISSN:
Container-title:2023 International Conference on High Performance Big Data and Intelligent Systems (HDIS)
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Author:

Zhou Qi¹,Yang Kai²,Ke Zunwang³,Wang Gang⁴,Zhang Yugui⁵,Jia Yizhen³,Cao Fengcai⁶,Ma Junxiao⁵,Liu Changlin¹,Zhang Kaijie¹,Wu Min⁵

Affiliation:

1. School of Semiconductor Science and Technology, South China Normal University,GuangZhou,China,510631

2. CGNPC Uranium Industry Development Co., Ltd.,Beijing,China,100020

3. School of Software, Xinjiang University,Urumqi,China,830046

4. School of Computing and Data Engineering, NingboTech University,Ningbo,China,315100

5. Institute of Semiconductors, Chinese Academy of Sciences,AnnLab,Beijing,China,100083

6. Zhongke Shangyi Health Technology (Beijing) Co., Ltd.,Beijing,China,100176

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10499438/10499451/10499557.pdf?arnumber=10499557

Reference32 articles.

1. A Dynamic Histogram Equalization for Image Contrast Enhancement

2. Contrast-limited adaptive histogram equalization: speed and effectiveness

3. Adaptive image contrast enhancement using generalizations of histogram equalization

4. Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms

5. Automatic Contrast-Limited Adaptive Histogram Equalization With Dual Gamma Correction

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