Fast Image Fusion Algorithm Based on Sparse Representation and Non-subsampled Contourlet Transform

ZHAO Chunhui; GUO Yunting

doi:10.11999/JEIT150933

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1773-1780

ZHAO Chunhui, GUO Yunting. Fast Image Fusion Algorithm Based on Sparse Representation and Non-subsampled Contourlet Transform[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1773-1780. doi: 10.11999/JEIT150933

Citation:

ZHAO Chunhui, GUO Yunting. Fast Image Fusion Algorithm Based on Sparse Representation and Non-subsampled Contourlet Transform[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1773-1780. doi: 10.11999/JEIT150933

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Fast Image Fusion Algorithm Based on Sparse Representation and Non-subsampled Contourlet Transform

doi: 10.11999/JEIT150933

ZHAO Chunhui¹,
GUO Yunting¹

Funds:

The National Natural Science Foundation of China (61571145, 61405041), The Key Program of Heilongjiang Province Natural Science Foundation (ZD201216), Excellent Academic Leaders Program of Harbin (RC2013XK009003)

Received Date: 2015-08-13
Rev Recd Date: 2016-04-07
Publish Date: 2016-07-19

Abstract

Abstract

In order to improve the efficiency and quality of image fusion, a new image fusion algorithm based on four-direction Sparse Representation (SR) and fast Non-Subsampled Contourlet Transform (NSCT) is proposed. The proposed method firstly provides a series of low- and high-frequency sub-bands of source images via fast NSCT decomposition. Then adaptive DCT over-complete dictionary is used for the fast four-direction sparse representation and coefficients fusion of low-pass sub-band, while Gaussian weighted regional energy based fusion rule are used in high-pass sub-bands. Fast NSCT modifies the tree structure filter bank of traditional NSCT into multi-channel structure, and it saves about half of the time. The fast SR fusion method adopts a four-direction sparse representation for coefficients fusion instead of traditional sliding window method, and further improves the efficiency of algorithm. The experimental results show that the proposed fast fusion algorithm can improve the efficiency nearly 20 times without sacrificing fusion quality.
- Image processing,
- Image fusion,
- Non-Subsampled Contourlet Transform (NSCT),
- Sparse Representation (SR),
- Fast algorithm

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References(21)

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