For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Variational-Bayesian Single-Image Devignetting
Motoharu SONOGASHIRA Masaaki IIYAMA Michihiko MINOH
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/09/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
vignetting, devignetting, single-image, variational Bayes,
Full Text: PDF(5.3MB)
>>Buy this Article
Vignetting is a common type of image degradation that makes peripheral parts of an image darker than the central part. Single-image devignetting aims to remove undesirable vignetting from an image without resorting to calibration, thereby providing high-quality images required for a wide range of applications. Previous studies into single-image devignetting have focused on the estimation of vignetting functions under the assumption that degradation other than vignetting is negligible. However, noise in real-world observations remains unremoved after inversion of vignetting, and prevents stable estimation of vignetting functions, thereby resulting in low quality of restored images. In this paper, we introduce a methodology of image restoration based on variational Bayes (VB) to devignetting, aiming at high-quality devignetting in the presence of noise. Through VB inference, we jointly estimate a vignetting function and a latent image free from both vignetting and noise, using a general image prior for noise removal. Compared with state-of-the-art methods, the proposed VB approach to single-image devignetting maintains effectiveness in the presence of noise, as we demonstrate experimentally.