Generalized Random Walks for Fusion of Multi-Exposure Images

A single captured image of a real-world scene is usually insufficient to reveal all the details due to under- or over-exposed regions. To solve this problem, images of the same scene can be first captured under different exposure settings and then combined into a single image using image fusion tech...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2011-12, Vol.20 (12), p.3634-3646
Main Authors: Rui Shen, Cheng, I., Jianbo Shi, Basu, A.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Applied sciences
Color
Computational efficiency
Consistency
Detection, estimation, filtering, equalization, prediction
Dynamic range
Exact sciences and technology
Image color analysis
Image enhancement
Image fusion
Image processing
Image reconstruction
Information, signal and communications theory
Laplace equations
Mathematical models
multi-exposure fusion
Optimization
Random walk
random walks
Signal and communications theory
Signal processing
Signal, noise
Studies
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A single captured image of a real-world scene is usually insufficient to reveal all the details due to under- or over-exposed regions. To solve this problem, images of the same scene can be first captured under different exposure settings and then combined into a single image using image fusion techniques. In this paper, we propose a novel probabilistic model-based fusion technique for multi-exposure images. Unlike previous multi-exposure fusion methods, our method aims to achieve an optimal balance between two quality measures, i.e., local contrast and color consistency, while combining the scene details revealed under different exposures. A generalized random walks framework is proposed to calculate a globally optimal solution subject to the two quality measures by formulating the fusion problem as probability estimation. Experiments demonstrate that our algorithm generates high-quality images at low computational cost. Comparisons with a number of other techniques show that our method generates better results in most cases.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2011.2150235