Sampling and Recovery of Binary Shapes via Low-Rank structures

Razavikia, Saeed; Zamani, Hamed; Amini, Arash

doi:10.1109/sampta45681.2019.9030896

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2024

Publication Types

Select...

Other3

Article2

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the reconstruction of binary shapes and images from their convolution with a finite length filter was considered in [7,8], where the authors utilize the sparse structure of the image to formulate a low-rank matrix recovery problem to obtain the desired image. Another instance of such finitevalue constraint occurs in the recovery of piece-wise constant images [1,9,10].…”

Section: Introductionmentioning

confidence: 99%

No Relaxation: Guaranteed Recovery of Finite-Valued Signals from Undersampled Measurements

Sarangi

Pal

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

This paper considers the problem of recovering a unipolar finite-valued signal from compressive measurements of its convolution with a known finite impulse response filter. We show that owing to the finite-value constraint the problem remains identifiable if the downsampling factor is smaller than the filter length. We develop a new computationally efficient decoding algorithm that can operate at the optimal downsampling factor under mild conditions on the filter. This allows us to explicitly impose the finite value constraint (no relaxation) without compromising on the computational tractability. 1

show abstract