TEFormer: Thermal Infrared Image Enhancement by Preserving Spatial Consistency and Details
Zhe Wu, Yunxin Li, Runmin Zhang, Si-Yuan Cao, Jiacheng Ying, Xiaohan Zhang, Xiaokai Bai, Shujie Chen, Bailin L. Yang, Hui‐Liang Shen · IEEE Transactions on Geoscience and Remote Sensing · 2025
Thermal infrared (TIR) images suffer from low contrast due to the atmospheric thermal radiation effect, especially under extreme conditions like low temperature. TIR image enhancement aims to improve image contrast, but previous enhancement approaches usually produce enhanced results with two limitations: spatial inconsistency and detail blurring. To deal with the limitations, we propose a novel TIR image enhancement method, named TEFormer, to preserve spatial consistency and restore fine-grained details during image enhancement. To preserve spatial consistency, we devise the global enhancement module (GEM) to enhance the low-resolution representation. The GEM performs long-range interactions across spatial dimensions and channel dimensions to condition the enhancement curve fitting. To keep details clear, we design the local enhancement module (LEM) as the decoding unit. The LEM injects additional detail structures into the enhanced low-resolution representation for high-resolution reconstruction. Besides, we further apply histogram-based supervision to facilitate learning in intensity distribution of clear images. Extensive experimental results on three challenging benchmarks demonstrate that the proposed method outperforms other state-of-the-art approaches.