294 citations · 928 across the 63 of their papers we have counts for
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Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The Method
Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A. Fessler
The sparsity of natural signals and images in a transform domain or dictionary has been extensively exploited in several applications such as compression, denoising and inverse pro…
FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications
Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. However, synthesis d…
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems
Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A. Fessler
The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adapt…
Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing
Saiprasad Ravishankar, Yoram Bresler
Compressed sensing is a powerful tool in applications such as magnetic resonance imaging (MRI). It enables accurate recovery of images from highly undersampled measurements by expl…
Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees
Saiprasad Ravishankar, Yoram Bresler
Many applications in signal processing benefit from the sparsity of signals in a certain transform domain or dictionary. Synthesis sparsifying dictionaries that are directly adapte…
Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI
Saiprasad Ravishankar, Yoram Bresler
Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT. This property has been heavily exploited in various applications…