WDTSNet: Wavelet Decomposition Two-Stage Network for Infrared Thermal Radiation Effect Correction

Recently, infrared thermal radiation effect correction methods are dominated by removing bias field in spatial domain. Since they do not consider the low-frequency characteristics of thermal radiation bias field and the high-frequency information of image content, these methods often fail in the enhancement of contrast and details. To address this problem, we propose a novel wavelet decomposition two-stage network for infrared thermal radiation effect correction, named WDTSNet. Through wavelet decomposition, we construct a low-frequency thermal radiation effect coarse correction subnetwork (LFCCSN) and a high-frequency detail enhancement fine correction subnetwork (HFFCSN), respectively. First, we take the small-size low-frequency component of the degraded image after discrete wavelet transformation (DWT) as the input of the first-stage LFCCSN and propose an intrablock multiscale residual dense module (IMRDM) to complete coarse correction and contrast enhancement through different scales of receptive fields and intrablock channel information interaction. Second, we perform inverse DWT (IDWT) to obtain the input of the second-stage HFFCSN and build a high-frequency gated residual module (HGRM) in HFFCSN to remove residual thermal radiation bias field and acquire the enhanced high-frequency information. In addition, we further design dual-branch cross-scale attention fusion module (DCAFM) between encoders and decoders to effectively aggregate the cross-scale information flow. Extensive experiments on simulated and real infrared images demonstrate that the proposed WDTSNet performs well on enhancing contrast and details than existing methods. The code will be publicly available upon acceptance.