2020
DOI: 10.3390/e22010118
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Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Abstract: Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images. Second, two initial weight maps are generated by filtering the two source images separately. Final weight maps are obtained by joint bilateral f… Show more

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Cited by 20 publications
(7 citation statements)
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“…Entropy [20] states the amount of information included in an image. The better the image fusion, the greater the entropy value.…”
Section: Entropymentioning
confidence: 99%
“…Entropy [20] states the amount of information included in an image. The better the image fusion, the greater the entropy value.…”
Section: Entropymentioning
confidence: 99%
“…Spatial data contain spatial information, and the basis of information measurement lies in the definition of spatial information. Shannon's information theory uses uncertainty as the definition of information and is widely used in urban sprawl analysis [32,33], landslide sensitivity analysis [34], and image processing applications such as band selection [35], image fusion [36,37], classification [38,39], and quality assessment [40]. In addition, Boltzmann entropy, which uses disorder information, has also made significant progress recently [23][24][25][26][27][28][29][30][31].…”
Section: Related Workmentioning
confidence: 99%
“…In the contribution by Liu et al [ 8 ], “Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter,” an image fusion method using multi-scale decomposition and joint sparse representation is introduced. There are five steps in this work.…”
Section: Themes Of This Special Issuementioning
confidence: 99%