Thermal Radiation Bias Correction for Infrared Images Using Huber Function-Based Loss

Limited by the imaging mechanism, thermal radiation emitted from infrared (IR) imaging devices commonly contaminates the detector response to the scene, causing an additive thermal radiation bias at each pixel that dramatically reduces the contrast of the image. This degradation is considered a bias field over the image, impacting the visual perception and subsequent applications. Therefore, eliminating the thermal radiation bias field is an urgent issue. This article proposes an adaptive thermal radiation bias field correction method using Huber function-based loss, which can adapt to image contents and thus preserve meaningful details while eliminating the bias field. The proposed method introduces the low-order bivariate polynomial surface model to fit the bias field from the observed image precisely. We establish a robust objective function-based on the Huber function to estimate parameters of the surface model, which can adaptively switch between the
$\ell _{1}$
-norm and the
$\ell _{2}$
-norm-based loss functions according to the image region. Thus, our method not only effectively maintains optimality in flat regions but also improves robustness in edge and texture regions. To balance efficiency and robustness, we propose an adaptive threshold that controls the behavior of the Huber function. For stable convergence, an improved gradient descent strategy is utilized to solve the Huber loss-based objective function with the two-direction fitting and an adjustable step. Both simulated and real experiments against classical and state-of-the-art methods demonstrate the superior performance of the proposed method in improving the contrast and preserving details.