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arXiv · 2411.16327

CapHDR2IR: Caption-Driven Transfer from Visible Light to Infrared Domain

Jingchao Peng·Thomas Bashford-Rogers·Zhuang Shao·Haitao Zhao·Aru Ranjan Singh·Abhishek Goswami·Kurt Debattista
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Abstract

Infrared (IR) imaging offers advantages in several fields due to its unique ability of capturing content in extreme light conditions. However, the demanding hardware requirements of high-resolution IR sensors limit its widespread application. As an alternative, visible light can be used to synthesize IR images but this causes a loss of fidelity in image details and introduces inconsistencies due to lack of contextual awareness of the scene. This stems from a combination of using visible light with a standard dynamic range, especially under extreme lighting, and a lack of contextual awareness can result in pseudo-thermal-crossover artifacts. This occurs when multiple objects with similar temperatures appear indistinguishable in the training data, further exacerbating the loss of fidelity. To solve this challenge, this paper proposes CapHDR2IR, a novel framework incorporating vision-language models using high dynamic range (HDR) images as inputs to generate IR images. HDR images capture a wider range of luminance variations, ensuring reliable IR image generation in different light conditions. Additionally, a dense caption branch integrates semantic understanding, resulting in more meaningful and discernible IR outputs. Extensive experiments on the HDRT dataset show that the proposed CapHDR2IR achieves state-of-the-art performance compared with existing general domain transfer methods and those tailored for visible-to-infrared image translation.

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Jingchao Peng, Thomas Bashford-Rogers, Zhuang Shao, Haitao Zhao, Aru Ranjan Singh, Abhishek Goswami, Kurt Debattista. 2024-11-25. CapHDR2IR: Caption-Driven Transfer from Visible Light to Infrared Domain. https://arxiv.org/abs/2411.16327

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