D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive Sensing
This work addresses the problem of image detail loss and inflexibility in compressive sensing reconstruction for researchers and practitioners in imaging, offering an incremental improvement over existing deep unfolding methods.
The paper tackles the limitation of deep unfolding networks in compressive sensing by introducing a dual-domain optimization framework that combines image- and convolutional-coding-domain priors, resulting in a network (D3C2-Net) that demonstrates superior performance and generalization across various 2D and 3D signals.
By mapping iterative optimization algorithms into neural networks (NNs), deep unfolding networks (DUNs) exhibit well-defined and interpretable structures and achieve remarkable success in the field of compressive sensing (CS). However, most existing DUNs solely rely on the image-domain unfolding, which restricts the information transmission capacity and reconstruction flexibility, leading to their loss of image details and unsatisfactory performance. To overcome these limitations, this paper develops a dual-domain optimization framework that combines the priors of (1) image- and (2) convolutional-coding-domains and offers generality to CS and other inverse imaging tasks. By converting this optimization framework into deep NN structures, we present a Dual-Domain Deep Convolutional Coding Network (D3C2-Net), which enjoys the ability to efficiently transmit high-capacity self-adaptive convolutional features across all its unfolded stages. Our theoretical analyses and experiments on simulated and real captured data, covering 2D and 3D natural, medical, and scientific signals, demonstrate the effectiveness, practicality, superior performance, and generalization ability of our method over other competing approaches and its significant potential in achieving a balance among accuracy, complexity, and interpretability. Code is available at https://github.com/lwq20020127/D3C2-Net.