scholarly journals Two approaches for log-compression parameter estimation: Comparative study

2009 ◽  
Vol 6 (3) ◽  
pp. 419-425 ◽  
Author(s):  
Milorad Paskas
Keyword(s):  
Parameter Estimation ◽  
Comparative Study ◽  
Dynamic Range ◽  
Signal Reconstruction ◽  
Ultrasound Images ◽  
Original Signal ◽  
Compression Parameter ◽  
Nonlinear Compression

Standard ultrasound devices perform nonlinear compression reducing dynamic range of the signal. In order to reconstruct original signal it is necessary to find out statistics of the signal before and signal after compression. There are two techniques for compression parameter estimation or, that is equivalent, compressed signal reconstruction advised in literature. In the paper we perform comparison of these techniques both for computer generated signals and ultrasound images. .

scholarly journals A Comparative Study of Three Speckle Reducing Methods for Intima-Media Thickness Ultrasound Images

2015 ◽  
Vol 17 (2) ◽  
Author(s):  
Mehravar Rafati ◽  
Masoud Arabfard ◽  
Mehrdad Rafati Rahimzadeh ◽  
Hasan Voshtani ◽  
Hassan Moladoust
Keyword(s):  
Comparative Study ◽  
Intima Media Thickness ◽  
Ultrasound Images ◽  
Media Thickness

scholarly journals Sparse Signal Recovery from Modulo Observations

2020 ◽  
Author(s):  
Viraj Shah ◽  
Chinmay Hegde
Keyword(s):  
Phase Retrieval ◽  
Dynamic Range ◽  
Signal Reconstruction ◽  
Real Data ◽  
Superior Performance ◽  
Signal Recovery ◽  
Reconstruction Methods ◽  
Novel Approach ◽  
Sparsity Constraints ◽  
Improved Performance

Abstract We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.

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