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- W4385804938 abstract "Generative Adversarial Networks (GANs) have shown an outstanding ability to generate high-quality images with visual realism and similarity to real images. This paper presents a new architecture for thermal image enhancement. Precisely, the strengths of architecture-based vision transformers and generative adversarial networks are exploited. The thermal loss feature introduced in our approach is specifically used to produce high-quality images. Thermal image enhancement also relies on fine-tuning based on visible images, resulting in an overall improvement in image quality. A visual quality metric was used to evaluate the performance of the proposed architecture. Significant improvements were found over the original thermal images and other enhancement methods established on a subset of the KAIST dataset. The performance of the proposed enhancement architecture is also verified on the detection results by obtaining better performance with a considerable margin regarding different versions of the YOLO detector." @default.
- W4385804938 created "2023-08-15" @default.
- W4385804938 creator A5049108338 @default.
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- W4385804938 date "2023-06-01" @default.
- W4385804938 modified "2023-09-25" @default.
- W4385804938 title "GAN-based Vision Transformer for High-Quality Thermal Image Enhancement" @default.
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- W4385804938 doi "https://doi.org/10.1109/cvprw59228.2023.00089" @default.
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