A Total Variation-Based Algorithm for Pixel-Level Image Fusion

被引：90

作者：

Kumar, Mrityunjay ^{[1
]}

Dass, Sarat ^{[2
]}

机构：

[1] Eastman Kodak Co, Res Labs, Rochester, NY 14650 USA

[2] Michigan State Univ, Dept Stat & Probabil, E Lansing, MI 48824 USA

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2009年 / 18卷 / 09期

关键词：

Eigenvector; forward model; image fusion; inverse problem; pixel-level fusion; total variation (TV);

D O I：

10.1109/TIP.2009.2025006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a total variation (TV) based approach is proposed for pixel-level fusion to fuse images acquired using multiple sensors. In this approach, fusion is posed as an inverse problem and a locally affine model is used as the forward model. A TV seminorm based approach in conjunction with principal component analysis is used iteratively to estimate the fused image. The feasibility of the proposed algorithm is demonstrated on images from computed tomography (CT) and magnetic resonance imaging (MRI) as well as visible-band and infrared sensors. The results clearly indicate the feasibility of the proposed approach.

引用

页码：2137 / 2143

页数：7

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