Aerial image restoration is an important low-level vision task for improving the quality and reliability of remote sensing and aerospace imagery. Compared with general natural image restoration，aerial image restoration faces more complex imaging conditions caused by several factors，including long-distance observation，platform motion，atmospheric interference，illumination variation，and sensor limitations. However，these factors often lead to diverse and coupled degradations，which not only reduce visual quality but also affect downstream tasks，such as object detection，semantic segmentation，change detection （CD），scene understanding，and autonomous perception. Therefore，robust aerial image restoration has become a key problem in both computer vision （CV） and aerospace information processing. In recent years，image restoration has evolved from task-specific models for individual degradations to unified restoration frameworks and large model-driven paradigms. To date，existing surveys mainly focus on general natural image restoration，specific low-level vision tasks，or individual degradation problems. In contrast，the present survey focuses on aerial image restoration from the perspective of large model empowerment. It also emphasizes the unique imaging characteristics and practical requirements of aerial scenarios，as well as systematically reviews the technical evolution from single-degradation restoration to unified modeling，and further to large model-driven intelligent restoration. In particular，this paper highlights the contributions of large-scale pretraining，multimodal representation learning，physical priors，and agent-based decision mechanisms to robust aerial image restoration under complex degradation conditions. First，this paper reviews representative restoration methods for typical single-degradation tasks and summarizes their basic modeling ideas，advantages，and limitations. Traditional methods usually rely on handcrafted priors，optimization models，and physical assumptions，all of which provide clear interpretability but are often limited in complex real-world scenarios. While deep learning-based methods improve restoration performance by learning nonlinear mappings from degraded images to clean images，many of them are still designed for specific degradation types and may lack generalization ability when facing unknown or mixed degradations in aerial scenes. To solve this problem，this paper discusses unified image restoration methods for complex aerial imaging scenarios. In practical applications，aerial image degradation is rarely caused by a single factor. Different degradation factors may coexist and interact with each other，resulting in complex coupled degradation and spatially non-uniform degradation distributions. As such，unified restoration methods aim to handle multiple degradations within a single framework. To date，existing studies have mainly explored multidegradation modeling，prompt-based restoration，and dynamic routing or expert-based frameworks. These methods improve the adaptability of restoration models by learning shared representations，introducing degradation-aware prompts，or dynamically selecting suitable restoration paths. However，challenges remain in terms of explicitly modeling degradation mechanisms，distinguishing different degradation patterns，and dealing with spatially varying degradations. On this basis，this survey focuses on large model-driven aerial image restoration. Vision foundation models provide transferable visual representations and structural priors through large-scale pretraining，which can improve restoration robustness under data-limited and cross-scene conditions. Multimodal large models introduce semantic and cross-modal information into the restoration process，thereby enabling models to better understand complex scenes and provide high-level guidance for structure recovery. The combination of physical models and large models provides a promising hybrid paradigm in which physical priors derived from imaging mechanisms constrain the restoration process，while large models provide strong representation and generation capabilities. Meanwhile，agent-based restoration methods extend image restoration from a static mapping process to a dynamic decision-making process，in which degradation analysis，strategy planning，model selection，feedback evaluation，and iterative optimization can be integrated into a unified restoration pipeline. Furthermore，this paper analyzes the main challenges faced by existing aerial image restoration methods. First，the modeling of coupled degradations remains insufficient，especially when multiple degradation factors interact in complex imaging environments. Second，existing collaborations between semantic priors and low-level visual features remain limited，making it difficult to achieve reliable structure recovery under severe degradation. Third，the integration of physical knowledge and data-driven large models remains underexplored，and balancing physical consistency with generative flexibility is still an open problem. Fourth，most existing methods lack dynamic adaptive mechanisms and are unable to adjust restoration strategies in accordance with image content，degradation state，or task requirements. Finally，the high computational cost of large model-driven methods restricts their deployment on airborne platforms，edge devices，and real-time remote sensing systems. Overall，aerial image restoration is moving from degradation-specific and pixel-level processing toward unified modeling，multimodal collaboration，physical prior-guided restoration，and intelligent decision-making. In summary，this survey aims to provide a systematic reference for researchers by clarifying the relationship between traditional restoration methods，unified restoration frameworks，and large model-driven paradigms. It also discusses future research directions，including multisource data construction，unified representation learning，multimodal and physical prior-guided restoration，adaptive decision-making，and efficient deployment，all of which are driven by the goal of promoting robust，interpretable，and intelligent aerial image restoration systems.