Simultaneous image fusion and denoising with adaptive sparse representation

By Yu Liu, Zengfu Wang. IET Image Processing, 2015

330 citations recorded by the source corpus.

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DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion (2020, methodology): “From top to bottom: PET image, MRI image, fusion results of DCHWT [55], DDCTPCA [14], ASR [56], Structure-Aware [57], FusionGAN [21] and RCGAN [58] the intensity of colors in the PET image, leading to the loss of functional information.”
Image Fusion With Convolutional Sparse Representation (2016, methodology): “Another advantage of most transform domain methods is that they can handle the fusion of noisy images by integrating the denoising process into the fusion framework [16], [18].”
Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity (2020, methodology): “The average run time of DCHWT, ASR, PCA and our PMGI is about 0.8636, 23.6055, 0.0046, and 0.0150 (unit: second).”
A medical image fusion method based on convolutional neural networks (2017, methodology): “To improve the fusion performance, many complex decomposition approaches and elaborate weight assignment strategies have been recently proposed in the literature [6], [8]–[13], [15], [16].”