When Fusion Meets Super-Resolution: Implicit Edge Calibration for Higher Resolution Multispectral Image Reconstruction

Multispectral image (MSI) reconstruction via remote sensing image fusion (RSIF), involving the fusion of MSIs with panchromatic (PAN) images, has attracted considerable attention. However, the main limitation of RSIF lies in the fact that the resolution of the fused image is limited by the PAN image. Although single image super-resolution (SISR) methods have the potential to improve image resolution, the larger scale factors often result in the loss of edge sharpness. To tackle these challenges, this article proposes a novel end-to-end framework for MSI reconstruction, termed FSRNet, aimed at generating higher resolution MSI while preserving the fine edge structural details. The FSRNet seamlessly integrates RSIF and SISR into a unified framework, thereby mitigating the cumulative errors that typically arise in two-stage cascaded methods. Moreover, an innovative implicit edge calibration (IEC) scheme is proposed that leverages gradient prior knowledge implicitly to calibrate and sharpen the MSI structure effectively. Notably, IEC is exclusively utilized during the training phase and is omitted during testing. Thus, it does not increase the complexity of the network in the actual application (i.e., testing phase). Additionally, we introduce the progress feature distillation module to improve feature representation through the feature distillation structure, facilitating the learning of more distinctive hierarchical features. Experimental results on the QuickBird, Gaofen-2, and WorldView-3 datasets demonstrate that the proposed method exhibits advantages compared to other state-of-the-art SISR and two-stage methods in terms of both quantitative and qualitative comparisons.