Abstract
Regularization-based image restoration has remained an
active research topic in image processing and computer vision. It often leverages a guidance signal captured in different fields as an additional cue. In this work, we present a
general framework for image restoration, called deeply aggregated alternating minimization (DeepAM). We propose
to train deep neural network to advance two of the steps in
the conventional AM algorithm: proximal mapping and ?-
continuation. Both steps are learned from a large dataset
in an end-to-end manner. The proposed framework enables
the convolutional neural networks (CNNs) to operate as a
regularizer in the AM algorithm. We show that our learned
regularizer via deep aggregation outperforms the recent
data-driven approaches as well as the nonlocal-based methods. The flexibility and effectiveness of our framework are
demonstrated in several restoration tasks, including single
image denoising, RGB-NIR restoration, and depth superresolution