LensBridge：频率引导的复合退化适应用于镜头像差校正和眩光去除
LensBridge: Frequency-Guided Compound Degradation Adaptation for Lens Aberration Correction and Veiling Glare Removal
- Zhejiang University（浙江大学）
- Hunan University（湖南大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
LensBridge提出两阶段框架，利用频率引导将可复用像差校正基础适应于复合退化，实现无需配对监督的联合像差校正与眩光去除。
AI中文摘要：
简化光学系统通常表现出残余镜头像差和眩光（VG），导致空间变化的模糊和对比度降低。大规模镜头库（LensLib）通过覆盖多样的点扩散函数（PSFs）实现可复用的像差校正模型，但其仅含像差的训练分布不包括目标特定的眩光。将此类基础扩展到复合退化具有挑战性，因为难以获得真实的目标系统复合配对。为应对此挑战，我们提出LensBridge，一个两阶段框架，首先建立可复用的像差校正基础，然后仅使用少量未配对的目标观测将其适应于复合光学退化。在第一阶段，我们通过从LensLib PSFs构建离散退化先验并学习直接从像差图像中检索它们，构建PSF感知的一步扩散基础，从而在推理时无需显式PSF即可实现PSF感知校正。在第二阶段，我们通过频域引导将此基础适应于复合退化。在数据层面，频率引导的退化完成（FDC）将目标低频特征转移到LensLib像差图像，同时保留像差结构以合成复合训练对；在模型层面，频率引导的伪分解（FPD）形成以像差和VG为主的伪观测，以条件化分离的适应分支。跨多个光学系统的广泛实验表明，LensBridge有效地将可复用的像差校正基础扩展到联合像差校正和眩光去除，而无需目标系统配对监督。所有代码将在此https URL提供。
英文摘要：
Simplified optical systems often exhibit residual lens aberrations and Veiling Glare (VG), resulting in spatially varying blur and contrast reduction. Large-scale Lens Libraries (LensLib) enable reusable aberration correction models by covering diverse Point Spread Functions (PSFs), but their aberration-only training distribution does not include target-specific veiling glare. Extending such foundations to compound degradation is challenging because realistic target-system compound pairs are difficult to obtain. To address this challenge, we propose LensBridge, a two-stage framework that first establishes a reusable aberration correction foundation and then adapts it to compound optical degradation using only a few unpaired target observations. In Stage I, we build a PSF-aware one-step diffusion foundation by constructing discrete degradation priors from LensLib PSFs and learning to retrieve them directly from aberrated images, enabling PSF-aware correction without requiring explicit PSF at inference. In Stage II, we adapt this foundation to compound degradation through frequency-domain guidance. At the data level, Frequency-guided Degradation Completion (FDC) transfers target low-frequency characteristics to LensLib aberrated images while preserving aberration structures to synthesize compound training pairs; at the model level, Frequency-guided Pseudo Decomposition (FPD) forms aberration- and VG-dominant pseudo observations to condition separate adaptation branches. Extensive experiments across multiple optical systems demonstrate that LensBridge effectively extends reusable aberration correction foundations to joint aberration correction and veiling glare removal without target-system paired supervision. All code will be available at https://github.com/XiaolongQian/LensBridge.
