OTPNet: ODE-Inspired Tuning-Free Proximal Network for Remote Sensing Image Fusion

Abstract

Remote sensing image fusion aims to reconstruct a high spatial and spectral resolution image by integrating the spatial and spectral information from multiple remote sensing sensor data. Despite the remarkable progress of deep learning-based fusion methods, most existing methods rely on manual network architecture design and hyperparameter tuning, lacking sufficient interpretability and adaptability. To address this limitation, we propose a novel neural Ordinary Differential Equation (ODE)-inspired tuning-free proximal splitting algorithm, which splits remote sensing image fusion as two optimization problems regularized by deep priors to model the fusion of spatial and spectral. Firstly, based on the physical properties of spatial and spectral information, the two problems are optimized by two proximal splitting operators to iteratively integrate spatial-spectral complementary information, eliminating or suppressing redundant information to reduce fusion errors. Secondly, considering the efficiency of neural ODE in reducing optimization error, we utilize a high-order numerical scheme to customize the proximal operator theoretically without additional handcrafted design and parameter tuning. Finally, by incorporating the numerical scheme as a solver into the proximal optimization algorithm, we derive an ODE-inspired Tuning-free Proximal Network, dubbed OTPNet, which achieves efficient and robust fusion reconstruction. Extensive experiments on nine datasets across three different remote sensing image fusion tasks show that our OTPNet outperforms existing state-of-the-art approaches, which validates the effectiveness of our method.