Remote sensing image fusion using the curvelet transform
By Filippo Nencini, Andrea Garzelli, Stefano Baronti, Luciano Alparone. Information Fusion, 2007
785 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/f4e9b062bf1b473c18180ab3d4fdbe9d0cacf3ed
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In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion (2021, methodology): “From top to bottom: infrared images, visible images, fused results of LPP [44], LP [45], CVT [46], DTCWT [47], GTF [24], CNN [37], FusionGAN [16], and our GANMcC. and adversarial loss.”
- A survey of classical methods and new trends in pansharpening of multispectral images (2011, background): “SAM is measured either in degrees or in radians and is usually averaged over the whole image to yield a global measurement of spectral distortion [107].”
- A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement (2011, background): “Image fusion is very important in that it aims to combine remotely sensed images that have different spectral and spatial resolutions with various applications such as visualization to identify the textures and shape of the various objects, feature extraction, and map updating [2].”
- A general framework for image fusion based on multi-scale transform and sparse representation (2015, neutral): “…[4], wavelet-based ones like discrete wavelet transform (DWT) [5], stationary wavelet transform (SWT) [6] and dual-tree complex wavelet transform (DTCWT) [7], and multi-scale geometric analysis (MGA)-based ones like curvelet transform (CVT) [8] and nonsubsampled con-tourlet transform (NSCT) [9].”
- FusionGAN: A generative adversarial network for infrared and visible image fusion (2019, methodology): “A In this section, we first briefly introduce the fusion metrics used in this paper and then demonstrate the efficacy of the proposed FusionGAN on public datasets, and compare it with eight state-of-the-art fusion methods including adaptive sparse representation (ASR) [37], curvelet transform (CVT) [38]”
- Simultaneous image fusion and denoising with adaptive sparse representation (2015, methodology): “The ASR-based method is compared with fi ve popular or latest TDF methods based on DWT [10], CVT [12], NSCT [13], ND [16] and SR [20].”
- Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images (2008, methodology): “On the one hand, multiresolution analysis (MRA) employing the discrete wavelet transform [11]–[13], the wavelet frames [5], [14], the Laplacian pyramid [6], or the curvelet transform [15] can be successfully adopted for spatial-detail extraction from the Pan image.”
- YDTR: Infrared and Visible Image Fusion via Y-Shape Dynamic Transformer (2023, methodology): “Fromtoptobottom:infraredimage,visible image,thefusionresultsofRP[8],CVT[9],DenseFuse[18],FusionGAN[19],U2Fusion[20],GANMcC[24],RFN-Nest[23],CSF[29],PPTFusion[32],and theproposedYDTR.”
- AttentionFGAN: Infrared and Visible Image Fusion Using Attention-Based Generative Adversarial Networks (2021, methodology): “The typical MST based methods include laplacian pyramid transform (LP) [8], wavelet transform (Wavelet) [9], ratio of low pass pyramid (RP) [10], multi-resolution singular value decomposition (MSVD) [11], complex wavelet transform (DTCWT) [12] and curvelet transform (CVT) [13].”
- Remote Sensing Image Fusion Based on Two-Stream Fusion Network (2017, methodology): “Previous researches such as ARSIS concept methods [9, 12] believe the spatial information lies in the high frequency of the PAN images.”