Boosting All-in-One UAV Image Restoration via Degradation-Aware Frequency Prompt State Space Model

Unmanned Aerial Vehicle (UAV)-captured images are easily affected by various degradations such as motion blur, noise, low illumination, haze, and raindrops in complex environments, and these degradations exhibit significant differences in the frequency domain. Existing all-in-one models typically operate in the spatial domain, making it difficult to effectively distinguish different degradation types, which leads to degradation interference and suboptimal restoration quality. To address these challenges, this paper proposes a degradation-aware prompt state space model for unified UAV image restoration. Specifically, we design a Prompt-Guided Mamba Block (PGMB) that injects dynamic degradation prompts into state space modeling, achieving differentiated global structure modeling. Meanwhile, we introduce an Adaptive Frequency Prompt Block (AFPB) that explicitly perceives frequency characteristics of diverse degradations and dynamically guides the reconstruction process via frequency-domain prompts, enabling collaborative restoration in both spatial and frequency domains. Extensive experiments demonstrate that the proposed method outperforms existing all-in-one approaches across multiple typical degradation tasks, significantly enhancing visual quality and robustness of UAV imagery.