用扩散模型先验提升湍流成像的清晰度与结构保真度。
Probabilistic Prior Driven Attention Mechanism Based on Diffusion Model for Imaging Through Atmospheric Turbulence
- 结合扩散模型先验与Transformer，建模湍流图像特征分布。
- 在湍流退化图像上实现更清晰、结构更保真的重建效果。
- 适合关注大气湍流成像修复的研究者与工程应用开发者。
大气湍流导致严重的空间和几何失真，挑战传统图像恢复方法。本文提出概率先验湍流去除网络（PPTRN），融合基于扩散模型的概率先验建模与Transformer驱动的特征提取，以应对该问题。PPTRN采用两阶段策略：首先在清晰图像上联合训练潜在编码器与Transformer，建立鲁棒特征表示；随后，通过去噪扩散概率模型（DDPM）对潜在向量的先验分布进行建模，指导Transformer捕捉恢复所需的多样化特征变化。核心创新在于概率先验驱动的交叉注意力机制，将DDPM生成的先验信息与特征嵌入融合，有效减少伪影并增强空间一致性。大量实验验证，PPTRN显著提升了湍流退化图像的恢复质量，在清晰度与结构保真度方面树立新基准。
原文摘要 · Abstract (English)
Atmospheric turbulence introduces severe spatial and geometric distortions, challenging traditional image restoration methods. We propose the Probabilistic Prior Turbulence Removal Network (PPTRN), which combines probabilistic diffusion-based prior modeling with Transformer-driven feature extraction to address this issue. PPTRN employs a two-stage approach: first, a latent encoder and Transformer are jointly trained on clear images to establish robust feature representations. Then, a Denoising Diffusion Probabilistic Model (DDPM) models prior distributions over latent vectors, guiding the Transformer in capturing diverse feature variations essential for restoration. A key innovation in PPTRN is the Probabilistic Prior Driven Cross Attention mechanism, which integrates the DDPM-generated prior with feature embeddings to reduce artifacts and enhance spatial coherence. Extensive experiments validate that PPTRN significantly improves restoration quality on turbulence-degraded images, setting a new benchmark in clarity and structural fidelity.
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