Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914986914217984 |
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| author | Lee, Dong-Guw Kim, Jeongyun Cho, Younggun Kim, Ayoung |
| author_facet | Lee, Dong-Guw Kim, Jeongyun Cho, Younggun Kim, Ayoung |
| contents | Thermal Infrared (TIR) imaging provides robust perception for navigating in challenging outdoor environments but faces issues with poor texture and low image contrast due to its 14/16-bit format. Conventional methods utilize various tone-mapping methods to enhance contrast and photometric consistency of TIR images, however, the choice of tone-mapping is largely dependent on knowing the task and temperature dependent priors to work well. In this paper, we present Thermal Chameleon Network (TCNet), a task-adaptive tone-mapping approach for RAW 14-bit TIR images. Given the same image, TCNet tone-maps different representations of TIR images tailored for each specific task, eliminating the heuristic image rescaling preprocessing and reliance on the extensive prior knowledge of the scene temperature or task-specific characteristics. TCNet exhibits improved generalization performance across object detection and monocular depth estimation, with minimal computational overhead and modular integration to existing architectures for various tasks. Project Page: https://github.com/donkeymouse/ThermalChameleon |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_18340 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images Lee, Dong-Guw Kim, Jeongyun Cho, Younggun Kim, Ayoung Robotics Computer Vision and Pattern Recognition Thermal Infrared (TIR) imaging provides robust perception for navigating in challenging outdoor environments but faces issues with poor texture and low image contrast due to its 14/16-bit format. Conventional methods utilize various tone-mapping methods to enhance contrast and photometric consistency of TIR images, however, the choice of tone-mapping is largely dependent on knowing the task and temperature dependent priors to work well. In this paper, we present Thermal Chameleon Network (TCNet), a task-adaptive tone-mapping approach for RAW 14-bit TIR images. Given the same image, TCNet tone-maps different representations of TIR images tailored for each specific task, eliminating the heuristic image rescaling preprocessing and reliance on the extensive prior knowledge of the scene temperature or task-specific characteristics. TCNet exhibits improved generalization performance across object detection and monocular depth estimation, with minimal computational overhead and modular integration to existing architectures for various tasks. Project Page: https://github.com/donkeymouse/ThermalChameleon |
| title | Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.18340 |