基于深度感知点扩散函数的单镜头可控景深成像研究
Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread Functions
AI总结：
针对极简光学系统（MOS）的光学像差和不可控景深问题，提出深度感知可控景深成像（DCDI）框架，结合深度感知退化自适应训练（DA2T）方案、DAMOS数据集及全镜头场模型，实现单镜头可控景深成像并提升恢复性能。
摘要：
可控景深（DoF）成像通常依赖笨重昂贵的高端镜头产生出色视觉效果，但随着移动场景需求增长，亟需极简光学系统（MOS）的轻量化方案。本研究针对MOS的严重光学像差和不可控景深两大局限，通过计算方法实现单镜头可控景深成像。提出深度感知可控景深成像（DCDI）框架，集成全焦（AiF）像差校正和单目深度估计模块，利用恢复图像与对应深度图，通过逐块卷积生成任意高端镜头在不同景深下的成像结果。为解决深度相关的光学退化问题，引入深度感知退化自适应训练（DA2T）方案；在数据集层面，基于不同物距下点扩散函数（PSF）的模拟，构建深度感知像差极简光学系统（DAMOS）数据集；此外设计两种即插即用的深度感知机制，将深度信息嵌入像差图像恢复以更好应对深度相关退化。还提出存储高效的全镜头场模型，用于表示多种镜头的4D PSF库。结合预测深度图、恢复图像及全镜头场模型推断的深度感知PSF图，实现单镜头可控景深成像。综合实验结果表明，该框架提升了恢复性能，获得了优异的单镜头可控景深成像结果，为该领域提供开创性基准。源代码与数据集将公开于https://github.com/XiaolongQian/DCDI。
英文摘要：
Controllable Depth-of-Field (DoF) imaging commonly produces amazing visual effects based on heavy and expensive high-end lenses. However, confronted with the increasing demand for mobile scenarios, it is desirable to achieve a lightweight solution with Minimalist Optical Systems (MOS). This work centers around two major limitations of MOS, i.e., the severe optical aberrations and uncontrollable DoF, for achieving single-lens controllable DoF imaging via computational methods. A Depth-aware Controllable DoF Imaging (DCDI) framework is proposed equipped with All-in-Focus (AiF) aberration correction and monocular depth estimation, where the recovered image and corresponding depth map are utilized to produce imaging results under diverse DoFs of any high-end lens via patch-wise convolution. To address the depth-varying optical degradation, we introduce a Depth-aware Degradation-adaptive Training (DA2T) scheme. At the dataset level, a Depth-aware Aberration MOS (DAMOS) dataset is established based on the simulation of Point Spread Functions (PSFs) under different object distances. Additionally, we design two plug-and-play depth-aware mechanisms to embed depth information into the aberration image recovery for better tackling depth-aware degradation. Furthermore, we propose a storage-efficient Omni-Lens-Field model to represent the 4D PSF library of various lenses. With the predicted depth map, recovered image, and depth-aware PSF map inferred by Omni-Lens-Field, single-lens controllable DoF imaging is achieved. Comprehensive experimental results demonstrate that the proposed framework enhances the recovery performance, and attains impressive single-lens controllable DoF imaging results, providing a seminal baseline for this field. The source code and the established dataset will be publicly available at https://github.com/XiaolongQian/DCDI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
