Single Image Super-Resolution Using Lightweight CNN with Maxout Units

Understand

Rectified linear units (ReLU) are well-known to be helpful in obtaining faster convergence and thus higher performance for many deep-learning-based applications.

• However, networks with ReLU tend to perform poorly when the number of filter parameters is constrained to a small number.
• To overcome it, in this paper, we propose a novel network utilizing maxout units (MU), and show its effectiveness on super-resolution (SR) applications.
• In general, the MU has been known to make the filter sizes doubled in generating the feature maps of the same sizes in classification problems.