PS-GAN: Pseudo Supervised Generative Adversarial Network With Single Scale Retinex Embedding for Infrared and Visible Image Fusion
Currently, ground truth fusion image, fused image contrast, and naturalness are rarely considered in existing infrared and visible image fusion (IVF) methods. In this article, we proposed a pseudosupervised generative adversarial network (GAN) with single scale retinex (SSR) embedding for IVF. First...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10783431/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850113920754253824 |
|---|---|
| author | Jin Qi Deboch Eyob Abera Jian Cheng |
| author_facet | Jin Qi Deboch Eyob Abera Jian Cheng |
| author_sort | Jin Qi |
| collection | DOAJ |
| description | Currently, ground truth fusion image, fused image contrast, and naturalness are rarely considered in existing infrared and visible image fusion (IVF) methods. In this article, we proposed a pseudosupervised generative adversarial network (GAN) with single scale retinex (SSR) embedding for IVF. First, a pseudoground truth fusion image conception and its computation method was proposed to solve ground truth fusion image shortage problem. Second, a novel SSR module embedded residual GAN was designed to improve fusion image contrast and naturalness. Finally, a special dense and mixed modal inputting strategy was also proposed for better modal mixed feature extraction. Extensive experimental results on public IVF datasets verified the superior performance of our proposed approach over other representative methods. It was demonstrated that the fused image details, contrast, and naturalness were significantly improved. |
| format | Article |
| id | doaj-art-167a2cc52433495aa8dfebb2a2d1b6f2 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-167a2cc52433495aa8dfebb2a2d1b6f22025-08-20T02:37:02ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01181766177710.1109/JSTARS.2024.350968410783431PS-GAN: Pseudo Supervised Generative Adversarial Network With Single Scale Retinex Embedding for Infrared and Visible Image FusionJin Qi0https://orcid.org/0000-0001-8427-0080Deboch Eyob Abera1Jian Cheng2https://orcid.org/0000-0001-6966-0531School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaCurrently, ground truth fusion image, fused image contrast, and naturalness are rarely considered in existing infrared and visible image fusion (IVF) methods. In this article, we proposed a pseudosupervised generative adversarial network (GAN) with single scale retinex (SSR) embedding for IVF. First, a pseudoground truth fusion image conception and its computation method was proposed to solve ground truth fusion image shortage problem. Second, a novel SSR module embedded residual GAN was designed to improve fusion image contrast and naturalness. Finally, a special dense and mixed modal inputting strategy was also proposed for better modal mixed feature extraction. Extensive experimental results on public IVF datasets verified the superior performance of our proposed approach over other representative methods. It was demonstrated that the fused image details, contrast, and naturalness were significantly improved.https://ieeexplore.ieee.org/document/10783431/Generative adversarial network (GAN)image contrastimage naturalnessinfrared (IR) and visible (VIS) image fusion (IVF)pseudoground truth fusion image (PGI) |
| spellingShingle | Jin Qi Deboch Eyob Abera Jian Cheng PS-GAN: Pseudo Supervised Generative Adversarial Network With Single Scale Retinex Embedding for Infrared and Visible Image Fusion IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Generative adversarial network (GAN) image contrast image naturalness infrared (IR) and visible (VIS) image fusion (IVF) pseudoground truth fusion image (PGI) |
| title | PS-GAN: Pseudo Supervised Generative Adversarial Network With Single Scale Retinex Embedding for Infrared and Visible Image Fusion |
| title_full | PS-GAN: Pseudo Supervised Generative Adversarial Network With Single Scale Retinex Embedding for Infrared and Visible Image Fusion |
| title_fullStr | PS-GAN: Pseudo Supervised Generative Adversarial Network With Single Scale Retinex Embedding for Infrared and Visible Image Fusion |
| title_full_unstemmed | PS-GAN: Pseudo Supervised Generative Adversarial Network With Single Scale Retinex Embedding for Infrared and Visible Image Fusion |
| title_short | PS-GAN: Pseudo Supervised Generative Adversarial Network With Single Scale Retinex Embedding for Infrared and Visible Image Fusion |
| title_sort | ps gan pseudo supervised generative adversarial network with single scale retinex embedding for infrared and visible image fusion |
| topic | Generative adversarial network (GAN) image contrast image naturalness infrared (IR) and visible (VIS) image fusion (IVF) pseudoground truth fusion image (PGI) |
| url | https://ieeexplore.ieee.org/document/10783431/ |
| work_keys_str_mv | AT jinqi psganpseudosupervisedgenerativeadversarialnetworkwithsinglescaleretinexembeddingforinfraredandvisibleimagefusion AT debocheyobabera psganpseudosupervisedgenerativeadversarialnetworkwithsinglescaleretinexembeddingforinfraredandvisibleimagefusion AT jiancheng psganpseudosupervisedgenerativeadversarialnetworkwithsinglescaleretinexembeddingforinfraredandvisibleimagefusion |