Localizing edges for estimating point spread function by removing outlier points

Abstract

This paper presents an approach to detect sharp edges for estimating point spread function (PSF) of a lens. A category of PSF estimation methods detect sharp edges from low-resolution (LR) images and estimate PSF with the detected edges. Existing techniques usually rely on accurate detection of ending points of the profile normal to an edge. In practice, however, it is often very difficult to localize profiles accurately. Inaccurately localized profiles generate a poor PSF estimation. We employ the Random Sample Consensus (RANSAC) algorithm to rule out outlier points. In RANSAC, prior knowledge about a pattern shape is incorporated, and the edge points lying far away from the pattern shape will be removed. The proposed method is tested on images of saddle patterns. Experimental results show that the proposed method can robustly localize sharp edges from LR saddle pattern images and yield accurate PSF estimation.