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Content Provider | IET Digital Library |
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Author | Fakhari, Fatemeh Mosavi, Mohammad. R. Lajvardi, Mehdi. M. |
Abstract | Image fusion is a process to enhance the human perception of different images from the same scene. Nowadays, two popular methods in the signal/image fusion, namely, multi-scale transform (MST) and sparse representation (SR) are being used. This study uses an image energy approach to enhance a fusion rule based on the combination of MST and SR methods. Each source image is first decomposed to its sub-bands using the selected MST method. Then, SR is applied to the low-pass band and maximum absolute (max-abs) rule merges the high-pass bands. The activity level of the sparse coefficients is measured based on the energy differences of the source images. When the gap energy is high enough, a coefficient with maximum L 2-norm is selected; otherwise, maximum L 1-norm is considered. Finally, by applying inverse MST to the attained bands, the fused image is reconstructed. The popular MSTs, such as discrete wavelet transform, dual-tree complex wavelet transform and non-sub-sampled contourlet are used. The experiments are carried out on several standard and real-life images. The measurement results confirm that the proposed method has enhanced the contrast, clarity and visual information of the fused results. |
Starting Page | 1041 |
Ending Page | 1049 |
Page Count | 9 |
ISSN | 17519659 |
Volume Number | 11 |
e-ISSN | 17519667 |
Issue Number | Issue 11, Nov (2017) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/11/11 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0104 |
Journal | IET Image Processing |
Publisher Date | 2017-07-18 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Combinatorial Mathematics Computer Vision And Image Processing Technique Discrete Wavelet Transform Dual-tree Complex Wavelet Transform Fused Image Reconstruction High-pass Band Image Energy Approach Image Fusion Image Human Perception Enhancement Image Reconstruction Image Representation Integral Transforms Inverse MST Method Inverse Transforms Low-pass Band Mathematics Maximum Absolute Rule Maximum L1-norm Maximum L2-norm Multiscale Transform Nonsub-sampled Contourlet Optical, Image And Video Signal Processing Real-life Image Source Image Decomposition Source Image Energy Difference Sparse Coefficient Sparse Representation SR Method Trees |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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