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Found 10 papers, 6 papers with code
Multi PILOT: Learned Feasible Multiple Acquisition Trajectories for Dynamic MRI
Several studies have particularly focused on applying deep learning techniques to learn these acquisition trajectories in order to attain better image reconstruction, rather than using some predefined set of trajectories.
LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data
In practice, desirable layout properties may not exist in a dataset, for instance, specific expert knowledge can be missing in the data.
Joint optimization of system design and reconstruction in MIMO radar imaging
Inspired by these successes, in this work, we propose to learn MIMO acquisition parameters in the form of receive (Rx) antenna elements locations jointly with an image neural-network based reconstruction.
Style Similarity as Feedback for Product Design
Matching and recommending products is beneficial for both customers and companies.
Image-Driven Furniture Style for Interactive 3D Scene Modeling
We propose a method for fast-tracking style-similarity tasks, by learning a furniture's style-compatibility from interior scene images.
Towards learned optimal q-space sampling in diffusion MRI
Fiber tractography is an important tool of computational neuroscience that enables reconstructing the spatial connectivity and organization of white matter of the brain.
3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI
Magnetic Resonance Imaging (MRI) has long been considered to be among the gold standards of today's diagnostic imaging.
PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI
Such schemes have already demonstrated substantial effectiveness, leading to considerably shorter acquisition times and improved quality of image reconstruction.
Joint learning of cartesian undersampling and reconstruction for accelerated MRI
On the other hand, recent works in optical computational imaging have demonstrated growing success of the simultaneous learning-based design of the acquisition and reconstruction schemes manifesting significant improvement in the reconstruction quality with a constrained time budget.
Self-supervised learning of inverse problem solvers in medical imaging
In light of this, we propose a self-supervised approach to training inverse models in medical imaging in the absence of aligned data.
