M. Patel, "Convolutional sparse and low-rank coding- based rain streak removal," in 2017 IEEE Winter Conference on Appli- cations of Computer Vision (WACV). IEEE, 2017, pp. 1-9.H. Zhang and V. M. Patel. Convolutional sparse coding- based image decomposition. In BMVC, 2016....
Convolutional Sparse and Low-Rank Coding-Based Rain Streak Removal He Zhang, Vishal M. Patel 2017 Semi-Supervised Transfer Learning for Image Rain Removal Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, Ying Wu 2018 Non-locally Enhanced Encoder-Decoder Network for...
These include low-rank approximation [7], network quantization [3, 12] and binarization [28, 6], weight pruning [12], dynamic inference [16], etc. However, most of these methods can only address one or two challenges mentioned above. Moreover, some of the techniques require specially desig...
represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input... convolutional neural networks, sparse coding 1 INTRODUCTION Single image super-resolution (SR) [20 keras例子-matchnet 对CNN来提取一对图像特征,然后通过欧氏距离(经典如Saimese网络)或者通过全...
Low rank constrained CNN’s achieved d times acceleration of non-constrained CNN’s. The computation cost of the direct convolution is Od2NCXY.. The computational cost of the proposed technique is OdKN+CXY.thus achieving acceleration of about d times. From the empirical analysis of the paper ...
Low-rank Decomposition approximates weight matrix in neural networks with low-rank matrix using techniques like Singular Value Decomposition (SVD) [7]. This method works especially well on fully-connected layers, yielding ∼3x model-size compression however without notable speed acceleration, since ...
Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition Comput Vis Image Und (2014) P. Felzenszwalb et al. Object detection with discriminatively trained part-based models IEEE Trans Pattern Anal Mach Intell (2010) D.G. Lowe Distinctive image fea...
Besides, sparse learning and a low-rank constraint are integrated with graph learning respectively to remove redundant information, and to obtain a compact graph structure for promoting information aggregation of GCNs. The experimental results show that the graph structure of our proposed graph learning...
Xu et al. [75] proposed the Bayesian deep matrix factorization (BDMF) for multiple image denoising. BDMF used the deep neural network (DNN) for low-rank components and optimization via stochastic gradient variation Bayes [76,77,78]. The network is a combination of the deep matrix factorizati...
NF-3DLogTNN: An effective hyperspectral and multispectral image fusion method based on nonlocal low-fibered-rank regularization 2023, Applied Mathematical Modelling Citation Excerpt : A hyperspectral image (HSI) composed of multiple intensities represents the integrals of the radiance captured by sensors...