2018/12/04 by Lucas Rencker, Francis Bach, Rencker, Lucas +5
Computer Science · Engineering · #FOS: Electrical engineering #Image and Signal Denoising Methods #Photoacoustic and Ultrasonic Imaging #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1812.01540
in Proceedings of iTWIST'18, Paper-ID: 4, Marseille, France, November, 21-23, 2018
arxiv created 2018/12/04 · openalex publication_date 2018/12/04 · arxiv updated 2018/12/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We address the problem of recovering a sparse signal from clipped or quantized measurements. We show how these two problems can be formulated as minimizing the distance to a convex feasibility set, which provides a convex and differentiable cost function. We then propose a fast iterative shrinkage/thresholding algorithm that minimizes the proposed cost, which provides a fast and efficient algorithm to recover sparse signals from clipped and quantized measurements.