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Jinsong Cheng

Publications

Citations

Publications by Year

20252025

Co-Authors:

Jiguang DaiHongzhou LiBing ZhangTengda ZhangRuishan Zhao

Jinsong Cheng

Affiliation

School of Geomatics

Liaoning Technical University

Fuxin, China

Publication Topics

Color Distribution,
Contrastive Loss,
Cycle Consistency Loss,
Deep Learning,
Deep Learning Models,
Fréchet Inception Distance,
Gaussian Mixture Model,
Generative Adversarial Networks,
Graph Neural Networks,
High Dynamic Range Image,
Image Enhancement Methods,
Image Patches

Biography

Jinsong Cheng was born in 1997. He is currently pursuing the master’s degree with Liaoning Technical University, Fuxin, China.,His research interests include computer vision and image analysis.(Based on document published on 10 January 2025).

Publications

Citations

Publications by Year

20252025

Co-Authors:

Jiguang Dai
Hongzhou Li
Bing Zhang
Tengda Zhang
Ruishan Zhao

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Land Satellite Remote Sensing Application Center, Ministry of Resources, Beijing, China(1)
School of Geomatics, Liaoning Technical University, Fuxin, China(1)
IEEE Transactions on Geoscience and Remote Sensing(1)
IEEE(1)
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RRCGAN: Unsupervised Compression of Radiometric Resolution of Remote Sensing Images Using Contrastive Learning

Tengda Zhang;Jiguang Dai;Jinsong Cheng;Hongzhou Li;Ruishan Zhao;Bing Zhang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

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The majority of current remote sensing images possess high-radiometric resolution exceeding 10 bits. Precisely compressing this radiometric resolution to 8 bits is crucial for visualization and subsequent deep learning tasks. Previously, radiometric resolution compression required extensive parameter adjustments of traditional tone-mapping operators. Deep learning is gradually replacing this high ...Show More

RRCGAN: Unsupervised Compression of Radiometric Resolution of Remote Sensing Images Using Contrastive Learning

Tengda Zhang;Jiguang Dai;Jinsong Cheng;Hongzhou Li;Ruishan Zhao;Bing Zhang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

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