BPConvNet for compressed sensing recovery in bioimaging
Kyong Jin, EPFL STI LIB

Meeting • 10 January 2017

Abstract
Iterative reconstruction methods have become the standard approach to solving inverse problems in imaging including denoising, deconvolution, and interpolation. With the appearance of compressed sensing, our theoretical understanding of these approaches evolved further with remarkable outcomes. These advances have been particularly influential in the field of biomedical imaging, e.g., in magnetic resonance imaging (MRI) and X-ray computed tomography (CT). A more recent trend is deep learning, which has arisen as a promising framework providing state-of-the-art performance for image classification and segmentation, regression-type neural networks. In this presentation, we explore the relationship between CNNs and iterative optimization methods for one specific class of inverse problems: those where the normal operator associated with the forward model is a convolution. Based on this connection, we propose a method for solving these inverse problems by combining a fast, approximate solver with a CNN. We demonstrate the approach on low-view CT reconstruction and accelerated MRI using residual learning and multilevel learning.