Predictive Filtering Integrated Generative Remote Sensing Hyperspectral Image Inpainting

Remote sensing hyperspectral images (HSIs) may contain incomplete or corrupted spatial or spectral information; how to reconstruct the missing regions to obtain a complete HSI is a challenging topic. Existing HSI inpainting methods are less generalizable across different remote sensing HSI and often contain artifacts. In order to address this problem, an HSI inpainting method based on predictive image filtering integrated generative restoration is proposed, which can adaptively handle different scenes with dynamically predicted filtering kernels according to different inputs. The generator of the proposed predictive filtering integrated generative HSI inpainting network (PFGIN) comprises two interconnected collaborative branches: the kernel prediction network (KPN) and the filtering-guided generative network (FGN). FGN provides features to KPN, and the KPN dynamically predicts the kernels of designed spatial-spectral filtering according to its input. Extensive experiments on the public datasets have demonstrated the effectiveness of the proposed method and its superiority over the other comparison methods. The code for PFGIN is publicly available at https://github.com/yinhuwu/PFGIN.