Multi-Focus Image Fusion Based on Adaptive Dual-Channel Spiking Cortical Model in Non-Subsampled Shearlet Domain

Liu, Shuaiqi; Wang, Jie; Lu, Yucong; Li, Hailiang; Zhao, Jie; Zhu, Zhihui

doi:10.1109/access.2019.2900376

Cited by 44 publications

(22 citation statements)

References 39 publications

(77 reference statements)

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“…Then, we use two separate loss functions to normalize the upper and lower channels. The loss functions of Net-S and Net-D are shown in formulas (11) and (12).…”

Section: A Image Super-resolutionmentioning

confidence: 99%

“…The methods based on the transform domain are generally implemented by three steps: image multi-scale decomposition, fusion of the coefficients generated by the transformation, and multi-scale reconstruction based on the fused coefficients. Among the representative algorithms is image fusion based on Laplace pyramid transform [7], discrete wavelet transform [8], curvelet transform [9], contourlet transform [10], and shearlet transform [11] and so on. Spatial domain-based methods include image fusion based on: max-min filtering [12], image block matching [13], guided filter [14], dense-scale feature-invariant methods [15] and multi-focus image fusion algorithms based on boundary discovery [16].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Then, we use two separate loss functions to normalize the upper and lower channels. The loss functions of Net-S and Net-D are shown in formulas (11) and (12).…”

Section: A Image Super-resolutionmentioning

confidence: 99%

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

Self Cite

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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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“…Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. Apart from the selection of transform methods, the fusion rules designed for merging transformed coefficients in high-or low-frequency domain also play a critical role in these methods, and many researches have also been taken in this direction [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Image Fusion With Point Detection Filter and Superpixel-Based Consistency Verification

Chen

Yang

et al. 2020

IEEE Access

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An accurate and efficient measurement of pixel's sharpness is a critical factor in most multi-focus image fusion methods. In our practice, we found that the focused regions get more blurred than the defocus regions when the multi-focus images are blurred digitally. Based on this observation, a novel multi-focus image fusion method is presented in this paper. In the given fusion scheme, focused regions detection is achieved by point detection filter and Gaussian filter, which has been certified more effective than other frequently used image clarity measures. Moreover, unlike the other commonly used consistency verification, we propose a superpixel-based consistency verification (SCV) method by integrating the image superpixels to improve the fusion performance. Image superpixels can perceptually represent meaningful image local features. Two datasets of multi-focus images are used to conduct experiments. Experimental results demonstrate that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both subjective visual perception and objective evaluation metrics.

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Multi-Focus Image Fusion Based on Adaptive Dual-Channel Spiking Cortical Model in Non-Subsampled Shearlet Domain

Cited by 44 publications

References 39 publications

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

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

Multi-Focus Image Fusion With Point Detection Filter and Superpixel-Based Consistency Verification

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