Single Image Super-Resolution Based on Capsule Neural Networks

TL;DR

This paper explores the application of capsule neural networks to single image super-resolution, demonstrating promising results with fewer layers and suggesting capsules as a valuable approach in this domain.

Contribution

It introduces capsule neural networks for SISR, showing their effectiveness and potential advantages over traditional convolutional networks.

Findings

Capsule networks achieve good super-resolution results with fewer layers.

Different strategies like new loss functions improve performance.

Capsules show promise as an alternative to traditional convolutions in SISR.

Abstract

Single image super-resolution (SISR) is the process of obtaining one high-resolution version of a low-resolution image by increasing the number of pixels per unit area. This method has been actively investigated by the research community, due to the wide variety of real-world problems where it can be applied, from aerial and satellite imaging to compressed image and video enhancement. Despite the improvements achieved by deep learning in the field, the vast majority of the used networks are based on traditional convolutions, with the solutions focusing on going deeper and/or wider, and innovations coming from jointly employing successful concepts from other fields. In this work, we decided to step up from the traditional convolutions and adopt the concept of capsules. Since their overwhelming results both in image classification and segmentation problems, we question how suitable they are for SISR. We also verify that different solutions share most of their configurations, and argue that this trend leads to fewer explorations of network varieties. During our experiments, we check various strategies to improve results, ranging from new and different loss functions to changes in the capsule layers. Our network achieved good results with fewer convolutional-based layers, showing that capsules might be a concept worth applying in the image super-resolution problem.