# Super-Resolution via Deep Learning

## TL;DR

This survey reviews deep learning-based super-resolution methods across images, videos, and depth maps, analyzing benchmarks, techniques, and results to provide a comprehensive overview of recent advances.

## Contribution

It offers a detailed survey of deep learning approaches to super-resolution, including benchmarks, critiques, and comparative analysis across multimedia domains.

## Key findings

- Deep learning has significantly advanced super-resolution performance.
- Benchmark datasets are crucial for evaluating SR methods.
- Current state-of-the-art methods outperform traditional approaches.

## 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.