IEEE Signal Processing Letters· 2016Q1
Image Fusion With Convolutional Sparse Representation
- 978citations
- Q1SCImago
- 2016year
Short summary
A new image fusion framework using convolutional sparse representation (CSR) decomposes images into base and detail layers, outperforming traditional sparse representation (SR) methods in detail preservation and misregistration robustness.
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Key points
- Introduces Convolutional Sparse Representation (CSR) for image fusion.
- CSR framework decomposes images into base and detail layers.
- Addresses limitations of traditional SR methods: detail preservation and misregistration sensitivity.
- Demonstrates superior performance over SR-based methods in objective and visual assessments.
AI-generated from the title and abstract; the full text is not read.
Abstract
As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion over the last few years with a number of effective algorithms being proposed. However, due to the patch-based manner applied in sparse coding, most existing SR-based fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to misregistration, while these two issues are of great concern in image fusion. In this letter, we introduce a recently emerged signal decomposition model known as convolutional sparse representation (CSR) into image fusion to address this problem, which is motivated by the observation that the CSR model can effectively overcome the above two drawbacks. We propose a CSR-based image fusion framework, in which each source image is decomposed into a base layer and a detail layer, for multifocus image fusion and multimodal image fusion. Experimental results demonstrate that the proposed fusion methods clearly outperform the SR-based methods in terms of both objective assessment and visual quality.
The authors' abstract, as published at the source. IEEE Signal Processing Letters, 2016 · DOI ↗
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Field: Media Technology
Media TechnologyEngineering