CircleFlow: Flow-Guided Camera Blur Estimation using a Circle Grid Target
This work addresses the problem of precise PSF estimation for optical characterization and computational vision, which is incremental as it builds on existing methods with a novel hybrid approach.
The paper tackled the challenge of accurately estimating the point spread function (PSF) for camera blur by introducing CircleFlow, a framework that uses flow-guided edge localization and a circle grid target, achieving state-of-the-art accuracy and reliability in experiments on simulated and real-world data.
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.