Single Image Super-Resolution Based on Capsule Neural Networks

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.


page 8

page 14

page 15

page 16


Real-World Single Image Super-Resolution Under Rainy Condition

Image super-resolution is an important research area in computer vision ...

CISRNet: Compressed Image Super-Resolution Network

In recent years, tons of research has been conducted on Single Image Sup...

Image Super-Resolution Using T-Tetromino Pixels

For modern high-resolution imaging sensors, pixel binning is performed i...

Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss

Single image super-resolution (SISR) is the task of inferring a high-res...

Single image super-resolution by approximated Heaviside functions

Image super-resolution is a process to enhance image resolution. It is w...

Efficient Single Image Super Resolution using Enhanced Learned Group Convolutions

Convolutional Neural Networks (CNNs) have demonstrated great results for...

Quantum Annealing for Single Image Super-Resolution

This paper proposes a quantum computing-based algorithm to solve the sin...

Please sign up or login with your details

Forgot password? Click here to reset