Polynomial Fitting-Based Estimation of Spatially Varying Point Spread Function from a Single Image

Point spread function (PSF) characterizes the intensity distribution characteristics of each object point and is widely used in areas such as defocus estimation, non-blind image deblurring, and computational imaging. Estimating spatially varying PSF from a single image is a typical inverse problem, which is constrained by multiple factors such as sensor noise and semantic information interference. In this paper, we propose a polynomial fitting-based method to model spatially varying PSF. With this method, we generate a large-scale, high-quality dataset with pixel-level annotations that can be used for training deep learning networks. To solve the task of estimating defocus maps from a single image, we design a novel high-resolution coefficient regression network to achieve accurate defocus estimation and concurrent estimation of multiple aberrations, respectively. To the best of our knowledge, this work presents the inaugural attempt at spatially varying PSF estimation based on polynomial coefficient regression. Extensive experimental results show that our methodology attains state-of-the-art performance across numerous evaluation metrics, fully verifying its effectiveness and superiority. The dataset and code is available on GitHub: https://github.com/67689E4F/PSFNet.git.