  While the depth of convolutional neural networks has attracted substantial
attention in the deep learning research, the width of these networks has
recently received greater interest. The width of networks, defined as the size
of the receptive fields and the density of the channels, has demonstrated
crucial importance in low-level vision tasks such as image denoising and
restoration. However, the limited generalization ability, due to the increased
width of networks, creates a bottleneck in designing wider networks. In this
paper, we propose the Deep Regulated Convolutional Network (RC-Net), a deep
network composed of regulated sub-network blocks cascaded by skip-connections,
to overcome this bottleneck. Specifically, the Regulated Convolution block
(RC-block), featured by a combination of large and small convolution filters,
balances the effectiveness of prominent feature extraction and the
generalization ability of the network. RC-Nets have several compelling
advantages: they embrace diversified features through large-small filter
combinations, alleviate the hazy boundary and blurred details in image
denoising and super-resolution problems, and stabilize the learning process.
Our proposed RC-Nets outperform state-of-the-art approaches with significant
performance gains in various image restoration tasks while demonstrating
promising generalization ability. The code is available at
https://github.com/cswin/RC-Nets.
