Neural Proximal Gradient Descent for Compressive Imaging

Understand

Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information.

• Learning a good inverse mapping from training data faces severe challenges, including: (i) scarcity of training data; (ii) need for plausible reconstructions that are physically feasible; (iii) need for fast reconstruction, especially in real-time applications.
• We develop a successful system solving all these challenges, using as basic architecture the recurrent application of proximal gradient algorithm.
• We learn a proximal map that works well with real images based on residual networks.