Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion
By Shutao Li, Haitao Yin, Leyuan Fang. IEEE Transactions on Biomedical Engineering, 2012
364 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/701b3b1c825828b82dcd44ad28c8a130191f4892
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Citations in the corpus
- Image denoising using deep CNN with batch renormalization (2020, background): “Since image-denoising techniques can recover original images well, and restore the details, they are widely applied in many fields, such as remote-sensing image [15] and medical image [37].”
- Pixel-level image fusion: A survey of the state of the art (2017, neutral): “…cross-scale fusion rule [40], guided filtering based weighted average [41] Sparse representation (SR) orthogonal matching pursuit[42, 43], group SR [44], E A gradient constrained SR [45], simultaneous OMP (SOMP) [46], joint sparsity model [47–49], SR with over-complete dictionary [50–53],…”
- FusionGAN: A generative adversarial network for infrared and visible image fusion (2019, methodology): “…are also inherent in nearly all objects [4 The image fusion problem has been developed with schemes including multi-scale transform-[5, 6, 7], sparse representation-[8, 9], neural network-[10, 11], subspace-[12, 13], and saliency-based [14, 15] methods, hybrid models ], and other methods [18, 19].”
- GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion (2021, methodology): “Typical traditional methods are sparse representation-based methods [7], [8], multiscale transform-based methods [9], [10], subspace-based methods [11], saliency-based method [12], [13], and hybrid methods [14], [15].”
- An overview of multi-modal medical image fusion (2016, methodology): “It is of interest to look at a snapshot of five different key methodologies: 1) color space [3, 4], 2) pyramid [5, 6], 3) wavelet [7-16], 4) sparse representation [17-21] and 5) salient feature [22-24].”
- A medical image fusion method based on convolutional neural networks (2017, methodology): “In image fusion research, sparse representation is another popular image modelling approach, which has also been successfully applied to fuse multi-modal medical images [4], [5], [15], [22].”