Image fusion based on complex-shearlet domain with guided filtering

Liu, Shuaiqi; Shi, Minghua; Zhu, Zhihui; Zhao, Jie

doi:10.1007/s11045-015-0343-6

Cited by 51 publications

(29 citation statements)

References 19 publications

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“…The larger the value of EN in the fused image is, the more information does the image contain, which means better image fusion result. And EN can be summarized as Equation (11).…”

Section: Objective Valuation Indexesmentioning

confidence: 99%

“…Recently, mainstream methods of image fusion have been based on the multi-scale transforms [8,9], such as image fusion based on object region detection and non-subsampled contourlet transform [10] and image fusion based on the complex shearlet transform with guided filtering [11]. For the image fusion method based on multi-scale transforms, the source images are represented by the fixed orthogonal basis functions, and the fused image can be obtained by fusing the coefficients of different sub-bands together in the transform domain.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Liu

et al. 2019

Electronics

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In this paper, a remote sensing image fusion method is presented since sparse representation (SR) has been widely used in image processing, especially for image fusion. Firstly, we used source images to learn the adaptive dictionary, and sparse coefficients were obtained by sparsely coding the source images with the adaptive dictionary. Then, with the help of improved hyperbolic tangent function (tanh) and l 0 − max , we fused these sparse coefficients together. The initial fused image can be obtained by the image fusion method based on SR. To take full advantage of the spatial information of the source images, the fused image based on the spatial domain (SF) was obtained at the same time. Lastly, the final fused image could be reconstructed by guided filtering of the fused image based on SR and SF. Experimental results show that the proposed method outperforms some state-of-the-art methods on visual and quantitative evaluations.

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“…The larger the value of EN in the fused image is, the more information does the image contain, which means better image fusion result. And EN can be summarized as Equation (11).…”

Section: Objective Valuation Indexesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Liu

et al. 2019

Electronics

Self Cite

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“…Generally, the existing image fusion algorithms can be divided into pixel-level fusion, feature-level fusion and decision-level fusion [3]. Pixel-level image fusion directly processes the single pixel of the images to generate the fusion result.…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Liu

Yin

et al. 2020

IEEE Access

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Multi-focus image fusion is an image processing that generates an integrated image by merging multiple images from different focus area in the same scene. For most fusion methods, the detection of the focus area is a critical step. In this paper, we propose a multi-focus image fusion algorithm based on a dual convolutional neural network (DualCNN), in which the focus area is detected from super-resolved images. Firstly, the source image is input into a DualCNN to restore the details and structure from its superresolved image, as well as to improve the contrast of the source image. Secondly, the bilateral filter is used to reduce noise on the fused image, and the guided filter is used to detect the focus area of the image and refine the decision map. Finally, the fused image is obtained by weighting the source image according to the decision map. Experimental results show that our algorithm can well retain image details and maintain spatial consistency. Compared with existing methods in multiple groups of experiments, our algorithm can achieve better visual perception according to subjective evaluation and objective indexes. INDEX TERMS Multi-focus image fusion, super-resolution, dual convolutional neural network, focus area detection.

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“…The other contains the wavelet transform-based methods, such as discrete wavelet transform (DWT) [ 7 ] and dual-tree complex wavelet transform (DTCWT) [ 8 ]. In addition, there are some new MSD methods, such as non-subsampled contourlet transform (NSCT) [ 9 ], shift-invariant shearlet transform (SIST) [ 10 ], non-subsampled shearlet transform (NSST) [ 11 ] and complex shearlet transform (CST) [ 12 ]. In recent years, the edge- preserving filter based-MSD has been a hot research direction.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Yudan

Yang

Zhang

et al. 2020

Entropy

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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 filtering according to the initial weight maps. Then, the multi-scale decomposition of the innovation images is performed through the rolling guide filter. Finally, the final weight maps are used to generate the fused innovation image. The fused innovation image and the common image are combined to generate the ultimate fused image. The experimental results show that our method’s average metrics are: mutual information ( M I )—5.3377, feature mutual information ( F M I )—0.5600, normalized weighted edge preservation value ( Q A B / F )—0.6978 and nonlinear correlation information entropy ( N C I E )—0.8226. Our method can achieve better performance compared to the state-of-the-art methods in visual perception and objective quantification.

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Image fusion based on complex-shearlet domain with guided filtering

Cited by 51 publications

References 19 publications

Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

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