Variational-Bayesian Single-Image Devignetting

Motoharu SONOGASHIRA  Masaaki IIYAMA  Michihiko MINOH  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.9   pp.2368-2380
Publication Date: 2018/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7393
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
Keyword: 
vignetting,  devignetting,  single-image,  variational Bayes,  

Full Text: PDF(5.3MB)
>>Buy this Article


Summary: 
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.