Solving Inverse Computational Imaging Problems Using Deep Pixel-Level Prior
Signal reconstruction is a challenging aspect of computational imaging as it often involves solving ill-posed inverse problems. Recently, deep feed…
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Sensing Matrix Design via Capacity Maximization for Block Compressive Sensing Applications
It is well-established in the compressive sensing (CS) literature that sensing matrices whose elements are drawn from independent random distributions exhibit enhanced reconstruction capabilities. In many CS applications, such as electromagnetic imaging, practical limitations on the measurement system prevent one from generating sensing matrices in this fashion.
Efficient Dynamic Parallel MRI Reconstruction for the Low-Rank Plus Sparse Model
The low-rank plus sparse (L+S) decomposition model enables the reconstruction of undersampled dynamic parallel magnetic resonance imaging data…
Read moreX-Ray Ghost-Tomography: Artefacts, Dose Distribution, and Mask Considerations
Ghost imaging has recently been successfully achieved in the X-ray regime. Due to the penetrating power of X-rays this immediately opens up the…
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