# Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective

arXiv: https://arxiv.org/abs/2303.03212 · HTML: https://mlsanity.com/paper/2303.03212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 64 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 22 of 22)

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [SRFlow: Learning the Super-Resolution Space with Normalizing Flow](https://mlsanity.com/paper/2006.14200/) (2020)
- [Part-based R-CNNs for Fine-grained Category Detection](https://mlsanity.com/paper/1407.3867/) (2014)
- [Handheld Multi-Frame Super-Resolution](https://mlsanity.com/paper/1905.03277/) (2019)
- [Deep Burst Super-Resolution](https://mlsanity.com/paper/2101.10997/) (2021)
- [HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery](https://mlsanity.com/paper/2002.06460/) (2020)
- [DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images](https://mlsanity.com/paper/1907.06490/) (2019)
- [Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks](https://mlsanity.com/paper/2007.03107/) (2020)
- [Structure-Preserving Image Super-Resolution](https://mlsanity.com/paper/2109.12530/) (2021)
- [Deep Learning for Multiple-Image Super-Resolution](https://mlsanity.com/paper/1903.00440/) (2019)
- [Toward Real-World Super-Resolution via Adaptive Downsampling Models](https://mlsanity.com/paper/2109.03444/) (2021)
- [Exploiting Raw Images for Real-Scene Super-Resolution](https://mlsanity.com/paper/2102.01579/) (2021)
- [Bayesian Image Super-Resolution with Deep Modeling of Image Statistics](https://mlsanity.com/paper/2204.00623/) (2022)
- [Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution](https://mlsanity.com/paper/2103.15290/) (2021)

## Abstract (arXiv)

Super-resolution is the process of obtaining a high-resolution image from one or more low-resolution images. Single image super-resolution (SISR) and multi-frame super-resolution (MFSR) methods have been evolved almost independently for years. A neglected study in this field is the theoretical analysis of finding the optimum combination of SISR and MFSR. To fill this gap, we propose a novel theoretical analysis based on the iterative shrinkage and thresholding algorithm. We implement and compare several approaches for combining SISR and MFSR, and simulation results support the finding of our theoretical analysis, both quantitatively and qualitatively.