Adaptive Contrast Enhancement of Cardiac Ultrasound Images using a Deep Unfolded Many-Body Quantum Algorithm
Résumé
Speckle has a considerable impact on medical ultrasound (US) imaging due to its its intrinsic random nature and spatially correlated behavior that severely reduces image contrast. In this paper, leveraging the quantum many-body theory, we propose a deep-learning architecture recasting a baseline denoising algorithm for adaptive contrast enhancement of US images. The proposed deep neural network integrates quantum mechanical concepts based on Schrödinger equations which makes our model robust for US image restoration. The potential of the proposed deep unfolded network is illustrated on simulated data and clinical cardiac US images. Both results show enhanced images with improved contrast and resolution while preserving underlying structures and significantly reducing the speckle noise.