93 citations · 524 across the 46 of their papers we have counts for
7 papers · 2 filters
Compressed Sensing via Universal Denoising and Approximate Message Passing
Yanting Ma, Junan Zhu, Dror Baron
We study compressed sensing (CS) signal reconstruction problems where an input signal is measured via matrix multiplication under additive white Gaussian noise. Our signals are ass…
A Universal Parallel Two-Pass MDL Context Tree Compression Algorithm
Nikhil Krishnan, Dror Baron
Computing problems that handle large amounts of data necessitate the use of lossless data compression for efficient storage and transmission. We present a novel lossless universal…
Two-Part Reconstruction with Noisy-Sudocodes
Yanting Ma, Dror Baron, Deanna Needell
We develop a two-part reconstruction framework for signal recovery in compressed sensing (CS), where a fast algorithm is applied to provide partial recovery in Part 1, and a CS alg…
A Parallel Two-Pass MDL Context Tree Algorithm for Universal Source Coding
Nikhil Krishnan, Dror Baron, Mehmet Kıvanç Mıhçak
We present a novel lossless universal source coding algorithm that uses parallel computational units to increase the throughput. The length- input sequence is partitioned into $…
Empirical Bayes and Full Bayes for Signal Estimation
Yanting Ma, Jin Tan, Nikhil Krishnan +1
We consider signals that follow a parametric distribution where the parameter values are unknown. To estimate such signals from noisy measurements in scalar channels, we study the…
Compressive Imaging via Approximate Message Passing with Image Denoising
Jin Tan, Yanting Ma, Dror Baron
We consider compressive imaging problems, where images are reconstructed from a reduced number of linear measurements. Our objective is to improve over existing compressive imaging…