Semi-Supervised Learning for Infrared Thermal Radiation Correction in the Real World

Infrared images are susceptible to thermal radiation. Infrared thermal radiation correction methods based on physical prior may fail while correcting real-world images, because assumed priors do not always hold in the real world, resulting in the presence of thermal radiation residuals. Supervised learning-based methods have the potential to achieve favorable outcomes in the correction of synthetic images. However, due to the unavailability of labeled datasets, their efficacy is limited when applied to real-world images. To address this problem, in this article, to the best of our knowledge, we propose the first semi-supervised learning network for infrared radiation correction in the real world, named SIRCNet. The network is trained using a semi-supervised strategy, which includes a supervised training stage and a self-supervised training stage. In the supervised training stage, we constructed a multilevel wavelet decomposition and reconstruction correction (MWDRC) module for latent image correction and an efficient generalized feature extraction (EGFE) module for bias field estimation. Furthermore, EGFE is composed of one partial channel interactive (PCI) attention block and three effective residual blocks (ERBs). Surface fitting can approximate the thermal radiation bias field of the thermal radiation degradation images. The fit bias field can provide critical prior knowledge that enhances EGFE’s estimation of the thermal radiation bias field. Hence, in the self-supervised training stage, when fine-tuning MWDRC and EGFE using a generator, surface fitting is employed to constrain EGFE. Comparative experiments demonstrate that SIRCNet outperforms existing correction methods on both real and synthetic datasets, achieving the best metrics as well as visualization.