Multifocus Image Fusion and Restoration With Sparse Representation

By Bin Yang, Shutao Li. IEEE Transactions on Instrumentation and Measurement, 2010

702 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/73260c1c27512f05385a04a8762db8cbd2e66bc8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Yu Liu
2. Zengfu Wang
3. Xun Chen
4. Xudong Kang
5. Jianwen Hu
6. Shuping Liu
7. Hu Peng
8. Z. J. Wang

In Computer Vision and Pattern Recognition

1. Yu Liu
2. Jiayi Ma
3. Xun Chen
4. Hui Li
5. Xiaojun Wu
6. Zengfu Wang
7. Leyuan Fang
8. Hu Peng

In Media Technology

1. Jiayi Ma
2. Yu Liu
3. Xun Chen
4. Hui Li
5. Xiaojun Wu
6. Zengfu Wang
7. Leyuan Fang
8. Hu Peng

Citations in the corpus

• Simultaneous image fusion and denoising with adaptive sparse representation (2015, methodology): “Sparse representation (SR) has been successfully employed in many image processing applications including denoising [17], super-resolution [18, 19] and fusion [20 – 25].”
• Pixel-level image fusion: A survey of the state of the art (2017, methodology): “The original sparsity based fusion work [42] adopts one class of mathematical functions to construct the dictionary.”
• Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion (2012, unknown): “In recent years, sparse representation has emerged as a powerful tool for image denoising [11], [12], image fusion [13], [14], magnetic resonance spectroscopy [15], voxel selection [16], electroencephalogram inverse problem [17], compressive sensing [18], etc.”
• Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019, methodology): “based on cartoon-texture decomposition (CTD) and applied a sparse representation (SR)-based fusion strategy [31] to merge the decomposed coefficients.”
• Multi-focus image fusion with a deep convolutional neural network (2017, methodology): “…multi-focus image fusion methods, which are the non-subsampled contourlet transform (NSCT)-based one [6] , the sparse representation (SR)-based one [13] , the NSCT-SR-based one [11] , the guided filtering (GF)-based one [25] , the multi-scale weighted gradient (MWG)-based one [27] and the dense…”
• Image matting for fusion of multi-focus images in dynamic scenes (2013, methodology): “Recently, some novel transform domain analysis methods, such as curvelet transform [5], contourlet transform [6–9], log-Gabor wavelet transform [10], support value transform [11], retina-inspired model [12] and sparse representation [13] are also applied to image fusion.”
• Image Fusion With Convolutional Sparse Representation (2016, methodology): “Since the design of activity level measure and fusion scheme in our methods generally follow the SR-based methods [16], [19], to make a fair and clear comparison between SR and CSR for image fusion (also considering the limitation of paper length), the proposed CSR-based methods are primarily”
• A general framework for image fusion based on multi-scale transform and sparse representation (2015, methodology): “For every fusion method which uses the sparse coding model, the local patch size is set to 8 (cid:2) 8 and the error tolerance e is set to 0.1 according to the analysis in [14].”