Super-resolution reconstruction of cardiac MRI Using coupled dictionary learning (2014)


High resolution 3D cardiac MRI is difficult to achieve due to the relative speed of motion occurring during acquisition. Instead, anisotropic 2D stack volumes are typical, and improving the resolution of these is strongly motivated by both visualisation and analysis. The lack of suitable reconstruction techniques that handle non-rigid motion means that cardiac image enhancement is still often attained by simple interpolation. In this paper, we explore the use of example-based super-resolution, to enable high fidelity patch-based reconstruction, using training data that does not need to be accurately aligned with the target data. By moving to a patch scale, we are able to exploit the data redundancy present in cardiac image sequences, without the need for registration. To do this, dictionaries of high-resolution and low-resolution patches are co-trained on high-resolution sequences, in order to enforce a common relationship between high- and low-resolution patch representations. These dictionaries are then used to reconstruct from a low-resolution view of the same anatomy. We demonstrate marked improvements of the reconstruction algorithm over standard interpolation.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution - 2018ABSTRACT:Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem.
PROJECT TITLE :Geometry-Consistent Light Field Super-Resolution via Graph-Based Regularization - 2018ABSTRACT:Light field cameras capture the 3D information in a very scene with one exposure. This special feature makes lightweight
PROJECT TITLE :A MAP-Based Approach for Hyperspectral Imagery Super-Resolution - 2018ABSTRACT:In this Project, we have a tendency to propose a novel single image Bayesian super-resolution (SR) algorithm where the hyperspectral
PROJECT TITLE :Wavelet-Based Single Image Super-Resolution With An Overall Enhancement Procedure - 2017ABSTRACT:In this paper, we have a tendency to address the problem of generating an excellent-resolution image primarily based
PROJECT TITLE : Learning Multiple Linear Mappings for Efficient Single Image Super-Resolution - 2015 ABSTRACT: Example learning-based mostly superresolution (SR) algorithms show promise for restoring a high-resolution (HR)

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry