Integrating Variational Modeling and Deep Learning for Removal of Mixed Complex Noise and Stripes in Hyperspectral Images
Abstract
Hyperspectral imagery (HSI) is highly susceptible to severe contamination by mixed noise during acquisition and transmission, including Gaussian noise, impulse noise, and complex multidirectional (e.g., oblique or curvilinear) stripes. This article proposes an optimization-inspired deep network for complex noise removal in HSI (OIC-Net) that integrates the mathematical interpretability of model-based optimization with the representational power of deep learning for HSI mixed noise removal. The proposed framework deeply unrolls an iterative physical solver into an end-to-end architecture, seamlessly unifying variational optimization with deep neural networks. In particular, OIC-Net constructs an interactively coupled, dual-branch subnetwork mechanism dedicated to abundance map restoration and stripe elimination, achieving progressive noise suppression through efficient latent variable interaction in a single forward pass. Tailored to complex real-world degradations, an omnidirectional convolutional module is designed to explicitly encode structural priors for the effective isolation of oblique and multidirectional stripes. Concurrently, the abundance restoration subnetwork employs an encoder-decoder architecture to guarantee highly accurate spatial structure reconstruction. The proposed framework possesses explicit mathematical interpretability while demonstrating superior cross-sensor generalization and computational efficiency, enabling robust noise isolation and high-fidelity detail restoration under severe real-world degradations.