TEFormer: Thermal Infrared Image Enhancement by Preserving Spatial Consistency and Details

Zhe Wu

Abstract:

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.