用小波域熵与条纹指数提升红外图像去噪速度与鲁棒性
TIDY: Thermal Infrared Image Denoising via Wavelet Domain Entropy and Directional Stripe Index
- 在小波域分解噪声与结构，实现高效精准抑制
- 推理速度达34Hz，比之前方法快得多
- 适合需要实时处理的机器人室内红外感知场景
热红外（TIR）成像因在弱光环境下具备强感知能力，被广泛应用于野外机器人，但其存在严重的随机噪声和固定模式噪声，影响下游估计精度。室内环境因热对比度低、温度分布均匀，噪声更显著，导致室内应用较少。现有去噪方法要么速度慢无法满足机器人在线部署需求，要么对严重退化不够鲁棒，且通常在合成噪声数据上训练。为此，我们提出TIDY，一种基于真实干净-噪声TIR数据训练的轻量级小波域去噪器。通过将去噪任务重构到小波域，TIDY显式解耦噪声与结构内容，实现针对性抑制并降低空间复杂度，显著提升推理速度（约34Hz）。TIDY引入两个新指标——小波熵与小波方向条纹指数，作为互补损失项，分别有效抑制随机噪声与条纹伪影。在严重室内退化及零样本设置下，TIDY显著提升鲁棒性，在热惯性里程计与单目深度估计等下游任务中持续取得性能提升。代码与数据集已开源：https://github.com/williamrheeth/TIDY
原文摘要 · Abstract (English)
Thermal infrared (TIR) imaging has been a popular choice for field robotics due to its robust perception capability under low light visual degradation, but it suffers from severe stochastic and fixed-pattern noise that breaks downstream estimation. This noise is intensified indoors due to low thermal contrast and uniform temperature distributions, contributing to the relative lack of indoor TIR deployments. Existing TIR denoising methods exhibit a poor accuracy-efficiency tradeoff, either too slow for online deployment required in robotics or insufficiently robust to severe degradation, while typically being trained on synthetic noise. Addressing these problems, we propose TIDY, a lightweight wavelet-domain denoiser trained on real clean-noisy TIR data. By reformulating TIR denoising in the wavelet domain, TIDY explicitly disentangles noise from structural content, enabling targeted suppression with reduced spatial complexity, significantly improving inference speed over prior methods (~34Hz). TIDY introduces two new metrics, Wavelet Entropy and Wavelet Directional Stripe Index, as complementary loss terms to explicitly suppress stochastic noise and stripe artifacts. Across severe indoor corruption and zero-shot settings, TIDY improves robustness and yields consistent gains in downstream robotics tasks including thermal inertial odometry and monocular depth estimation. Code and dataset is available at: https://github.com/williamrheeth/TIDY
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