Super-Resolution via Deep Learning

Abstract

The recent phenomenal interest in convolutional neural networks (CNNs) must have made it inevitable for the super-resolution (SR) community to explore its potential. The response has been immense and in the last three years, since the advent of the pioneering work, there appeared too many works not to warrant a comprehensive survey. This paper surveys the SR literature in the context of deep learning. We focus on the three important aspects of multimedia - namely image, video and multi-dimensions, especially depth maps. In each case, first relevant benchmarks are introduced in the form of datasets and state of the art SR methods, excluding deep learning. Next is a detailed analysis of the individual works, each including a short description of the method and a critique of the results with special reference to the benchmarking done. This is followed by minimum overall benchmarking in the form of comparison on some common dataset, while relying on the results reported in various works.

References in corpus (14)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• FlowNet: Learning Optical Flow with Convolutional Networks
• Is the deconvolution layer the same as a convolutional layer?
• Failures of Gradient-Based Deep Learning
• End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
• Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
• ATGV-Net: Accurate Depth Super-Resolution
• A Deep Primal-Dual Network for Guided Depth Super-Resolution
• Single Image Super Resolution - When Model Adaptation Matters
• Joint convolutional neural pyramid for depth map super-resolution
• Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
• Learning a Mixture of Deep Networks for Single Image Super-Resolution

Cited by in corpus (5)

• Optimal Physical Preprocessing for Example-Based Super-Resolution
• Neural Knitworks: Patched Neural Implicit Representation Networks
• Learned Multi-View Texture Super-Resolution
• Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images
• Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling