Accurately estimating PSF with straight lines detected by Hough transform

Ruichen Wang; Liangpeng Xu; Chunxiao Fan; Yong Li

doi:10.1117/12.2302699

10 April 2018 Accurately estimating PSF with straight lines detected by Hough transform

Ruichen Wang, Liangpeng Xu, Chunxiao Fan, Yong Li

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106151N (2018) https://doi.org/10.1117/12.2302699
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

This paper presents an approach to estimating point spread function (PSF) from low resolution (LR) images. Existing techniques usually rely on accurate detection of ending points of the profile normal to edges. In practice however, it is often a great challenge to accurately localize profiles of edges from a LR image, which hence leads to a poor PSF estimation of the lens taking the LR image. For precisely estimating the PSF, this paper proposes firstly estimating a 1-D PSF kernel with straight lines, and then robustly obtaining the 2-D PSF from the 1-D kernel by least squares techniques and random sample consensus. Canny operator is applied to the LR image for obtaining edges and then Hough transform is utilized to extract straight lines of all orientations. Estimating 1-D PSF kernel with straight lines effectively alleviates the influence of the inaccurate edge detection on PSF estimation. The proposed method is investigated on both natural and synthetic images for estimating PSF. Experimental results show that the proposed method outperforms the state-ofthe- art and does not rely on accurate edge detection.

Citation Download Citation

Ruichen Wang, Liangpeng Xu, Chunxiao Fan, and Yong Li "Accurately estimating PSF with straight lines detected by Hough transform", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151N (10 April 2018); https://doi.org/10.1117/12.2302699

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available