2 citations · 2 across the 2 of their papers we have counts for
4 papers
Bregman Plug-and-Play Priors
Abdullah H. Al-Shabili, Xiaojian Xu, Ivan Selesnick +1
The past few years have seen a surge of activity around integration of deep learning networks and optimization algorithms for solving inverse problems. Recent work on plug-and-play…
Image Fusion via Sparse Regularization with Non-Convex Penalties
Nantheera Anantrasirichai, Rencheng Zheng, Ivan Selesnick +1
The L1 norm regularized least squares method is often used for finding sparse approximate solutions and is widely used in 1-D signal restoration. Basis pursuit denoising (BPD) perf…
Sparse Regularization via Convex Analysis
Ivan Selesnick
Sparse approximate solutions to linear equations are classically obtained via L1 norm regularized least squares, but this method often underestimates the true solution. As an alter…
Sparse Frequency Analysis with Sparse-Derivative Instantaneous Amplitude and Phase Functions
Yin Ding, Ivan W. Selesnick
This paper addresses the problem of expressing a signal as a sum of frequency components (sinusoids) wherein each sinusoid may exhibit abrupt changes in its amplitude and/or phase.…