Restoration of Blurred Binary Images Using Discrete Tomography

Németh, József; Balázs, Péter

doi:10.1007/978-3-319-02895-8_8

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Cited by 3 publications

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“…In this paper, our focus will be to recover an unknown signal which takes values from a finite set. Such finite-valued signals naturally arise in applications such as medical imaging [1,2], image segmentation [3], neural spike detection [4] and wireless communication [5,6].…”

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)

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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

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