Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns.
Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising.
#8 best model for Recommendation Systems on MovieLens 1M
We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a "shallow" dictionary learning model with augmentation.
Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries.
Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns.
In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures.
All current non-rigid structure from motion (NRSfM) algorithms are limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle.