Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing


In recent years, Deep Neural Networks (DNN) have empowered Compressed Sensing (CS) substantially and have achieved high reconstruction quality and speed far exceeding traditional CS methods. However, there are still lots of issues to be further explored before it can be practical enough. There are mainly two challenging problems in CS, one is to achieve efficient data sampling, and the other is to reconstruct images with high-quality. To address the two challenges, this paper proposes a novel Runge-Kutta Convolutional Compressed Sensing Network (RK-CCSNet). In the sensing stage, RK-CCSNet applies Sequential Convolutional Module (SCM) to gradually compact measurements through a series of convolution filters. In the reconstruction stage, RK-CCSNet establishes a novel Learned Runge-Kutta Block (LRKB) based on the famous Runge-Kutta methods, reformulating the process of image reconstruction as a discrete dynamical system. Finally, the implementation of RK-CCSNet achieves state-of-the-art performance on influential benchmarks with respect to prestigious baselines, and all the codes are available at

In European Conference on Computer Vision 2020

Combined compressed sensing and deep learning, proposed RK-CCSNet to improve both the encoding and reconstruction phase and achieved state-of-the-art results in deep compressed sensing

Daolang Huang
Daolang Huang
Doctoral candidate at Aalto University

My research interests include amortized inference, deep learning, probabilistic machine learning and audio information retrieval