Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images

• URL: http://arxiv.org/abs/2410.18340v1
• Date: Thu, 24 Oct 2024 00:33:26 GMT
• Title: Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images
• Authors: Dong-Guw Lee, Jeongyun Kim, Younggun Cho, Ayoung Kim,
• Abstract summary: Thermal Chameleon Network (TCNet) is a task-adaptive tone-mapping approach for 14-bit TIR images. Given the same image, TCNet tone-maps different representations of TIR images tailored for each specific task. TCNet exhibits improved generalization performance across object detection and monocular depth estimation.
• Score: 17.785948496883506
• License:
• Abstract: 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

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