Dual-tree biquaternion wavelet transform and its application to color image fusion

Cao, Xiang

doi:10.1016/j.sigpro.2020.107513

Cited by 22 publications

(1 citation statement)

References 44 publications

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“…The MRA method decomposes the source image into different scale spaces using multiscale transformation methods first, such as Laplacian pyramid transform [15,16], wavelet transform [17][18][19][20], contourlet transform [21,22] and curvelet transform [23,24], non-subsampled contourlet transform (NSCT) [25][26][27], non-subsampled shearlet transform (NSST) [28]. Then, the image is fused in different scale space.…”

Section: Introductionmentioning

confidence: 99%

DAFCNN: A Dual-Channel Feature Extraction and Attention Feature Fusion Convolution Neural Network for SAR Image and MS Image Fusion

et al. 2023

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In the field of image fusion, spatial detail blurring and color distortion appear in synthetic aperture radar (SAR) images and multispectral (MS) during the traditional fusion process due to the difference in sensor imaging mechanisms. To solve this problem, this paper proposes a fusion method for SAR images and MS images based on a convolutional neural network. In order to make use of the spatial information and different scale feature information of high-resolution SAR image, a dual-channel feature extraction module is constructed to obtain a SAR image feature map. In addition, different from the common direct addition strategy, an attention-based feature fusion module is designed to achieve spectral fidelity of the fused images. In order to obtain better spectral and spatial retention ability of the network, an unsupervised joint loss function is designed to train the network. In this paper, the Sentinel 1 SAR images and Landsat 8 MS images are used as datasets for experiments. The experimental results show that the proposed algorithm has better performance in quantitative and visual representation when compared with traditional fusion methods and deep learning algorithms.

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