Abstract
The point spread function (PSF) serves as a fundamental descriptor linking the real-world scene to the captured signal, manifesting as camera blur. Accurate PSF estimation is crucial for both optical characterization and computational vision, yet remains challenging due to the inherent ambiguity and the ill-posed nature of intensity-based deconvolution. We introduce CircleFlow, a high-fidelity PSF estimation framework that employs flow-guided edge localization for precise blur characterization. CircleFlow begins with a structured capture that encodes locally anisotropic and spatially varying PSFs by imaging a circle grid target, while leveraging the target's binary luminance prior to decouple image and kernel estimation. The latent sharp image is then reconstructed through subpixel alignment of an initialized binary structure guided by optical flow, whereas the PSF is modeled as an energy-constrained implicit neural representation. Both components are jointly optimized within a demosaicing-aware differentiable framework, ensuring physically consistent and robust PSF estimation enabled by accurate edge localization. Extensive experiments on simulated and real-world data demonstrate that CircleFlow achieves state-of-the-art accuracy and reliability, validating its effectiveness for practical PSF calibration.
Abstract (translated)
点扩散函数（PSF）是将现实场景与捕获信号联系起来的基本描述符，表现为相机模糊。准确的PSF估计对于光学特性和计算视觉至关重要，但由于基于强度的去卷积固有的歧义和不适定性质而具有挑战性。我们介绍了CircleFlow，这是一种高保真度的PSF估算框架，采用流动引导边缘定位以精确表征模糊。CircleFlow从结构化捕捉开始，通过成像圆形网格目标来编码局部各向异性和空间变化的PSFs，并利用目标二进制亮度先验将图像和核估计解耦。然后，通过光学流引导初始化的二进制结构进行亚像素对齐重建潜在清晰图像，而PSF则被建模为能量受限的隐式神经表示。这两个组件在考虑到拜耳去马赛克处理的可微框架中联合优化，确保由准确边缘定位支持的物理一致性和稳健的PSF估计。在模拟和真实数据上的广泛实验表明，CircleFlow达到了最先进的精度和可靠性，验证了其在实际PSF校准中的有效性。
URL
https://arxiv.org/abs/2512.00796
