Joint compressive sampling and deconvolution in ultrasound medical imaging

Zhouye Chen (1) , Adrian Basarab (1, 2) , Denis Kouamé (1)

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

The interest of compressive sampling and image deconvolution has been extensively explored in the ultrasound imaging literature. The first seeks to reduce the volume of acquired data and/or to accelerate the frame rate. The second aims at improving the ultrasound image quality in terms of spatial resolution, contrast and signal to noise ratio. In this paper, we propose a novel approach combining these two frameworks, resulting into a compressive deconvolution technique aiming at obtaining high quality reconstructions from a reduced number of measurements. The resulting inverse problem is solved by minimizing an objective function taking into account the data attachment term and two appropriate prior information terms adapted to ultrasound imaging.

Mots clés: Compressive deconvolution; Deconvolution; Compressive sampling; Image enhancement

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, Adrian Basarab, Denis Kouamé. Joint compressive sampling and deconvolution in ultrasound medical imaging. IEEE International Ultrasonics Symposium (IUS 2015), Oct 2015, Taipei, Taiwan. pp. 1-4. ⟨hal-01363371⟩