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Learning based methods have shown very promising results for the task of depth estimation in single images.
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function.
We propose a universal image reconstruction method to represent detailed images purely from binary sparse edge and flat color domain.
Ranked #1 on Image Reconstruction on Edge-to-Shoes
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition.
Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.
A combination of Deep CNNs and Skip connection layers is used as a feature extractor for image features on both local and global area.