Ultrasound compressive deconvolution with lp-norm prior

Zhouye Chen (1) , Ningning Zhao (2, 1) , Adrian Basarab (1, 3) , Denis Kouamé (1)

1 IRIT-TCI - Traitement et Compréhension d’Images
2 IRIT-SC - Signal et Communications
3 UT3 - Université Toulouse III - Paul Sabatier

It has been recently shown that compressive sampling is an interesting perspective for fast ultrasound imaging. This paper addresses the problem of compressive deconvolution for ultrasound imaging systems using an assumption of generalized Gaussian distributed tissue reflectivity function. The benefit of compressive deconvolution is the joint volume reduction of the acquired data and the image resolution improvement. The main contribution of this work is to apply the framework of compressive deconvolution on ultrasound imaging and to propose a novel ℓp-norm (1 ≤ p ≤ 2) algorithm based on Alternating Direction Method of Multipliers. The performance of the proposed algorithm is tested on simulated data and compared with those obtained by a more intuitive sequential compressive deconvolution method.

Mots clés: Ultrasound imaging; Compressive sampling; Alternating direction method of multipliers; Deconvolution; Generalized Gaussian distribution

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Zhouye Chen, Ningning Zhao, Adrian Basarab, Denis Kouamé. Ultrasound compressive deconvolution with lp-norm prior. 23rd European Signal and Image Processing Conference (EUSIPCO 2015), Aug 2015, Nice, France. pp. 2791-2795. ⟨hal-01371792⟩