A Unified Framework for Double-Degradation Remote Sensing Image Restoration Through Saliency-Guided Interaction Learning

Remote sensing images (RSIs) are often exposed to various degradation factors such as sensor noise and poor observation environments. These factors can result in the loss of image details, spectral distortion, and blurred scenes, which will severely hinder the performance of subsequent RSI applications. Unfortunately, current methods are only capable of addressing individual degradation factors, and lack the ability to handle multiple degradation factors within a unified framework. To mitigate this problem, we model the complex degradation process as a double-degradation model for RSIs, and propose a unified framework based on saliency-guided interaction learning (SGIL) for double-degradation RSI restoration. The proposed SGIL can simultaneously alleviate the influence of external environment degradation and internal sensor noise degradation, which comprises three parts: pseudo pixel supervision-based saliency analysis (PPS-SA), a task-aware interaction learning (TAIL) model, and a global feature enhancement module (GFEM). In PPS-SA, an explicit PPS-SA method is designed to generate saliency maps to effectively distinguish different texture complexities of RSIs, and a saliency-guided mapping selection mechanism is introduced to adapt to complex external environmental interference factor. In the TAIL model, a dehazing module and a super resolution (SR) module are specialized in alleviating the external environment interference and internal sensor noise, respectively. Instead of simple cascading, the two modules interact and collaborate with each other, which drastically improves the performance of double-degradation image restoration. To further improve the performance of the proposed SGIL, we also propose the GFEM to exploit global features and refine the restored results. Experiments on several RSI datasets demonstrate that the proposed SGIL achieves promising results on complex double-degradation RSIs.

I. Introduction

Remote sensing images (RSIs) are vulnerable to various degradation factors during imaging, leading to a range of distortions and low-quality issues. Some of these issues include ground object occlusions, detail loss, and scene blurring. These factors can pose a significant challenge to effective interpretation of the Earth. To overcome these issues, RSI restoration techniques are commonly employed to improve image quality, thereby providing high-quality data for subsequent applications such as semantic segmentation [1], change detection [2], and environmental monitoring [3].