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Lookup NU author(s): Dr Zhuang Shao
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Peng J, Bashford-Rogers T, Shao Z, Zhao H, Singh AR, Goswami A, Debattista K
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Multimedia
Year: 2026
Pages: Epub ahead of print
Online publication date: 06/01/2026
Acceptance date: 02/04/2018
Date deposited: 26/01/2026
ISSN (print): 1520-9210
ISSN (electronic): 1941-0077
Publisher: IEEE
URL: https://doi.org/10.1109/TMM.2026.3651090
DOI: 10.1109/TMM.2026.3651090
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