# Ensemble Super-Resolution with A Reference Dataset

arXiv: https://arxiv.org/abs/1905.04696 · HTML: https://mlsanity.com/paper/1905.04696/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2019.

## Built on by (most cited first, 5 of 5)

- [FiRe: Fixed-points of Restoration Priors for Solving Inverse Problems](https://mlsanity.com/paper/2411.18970/) (2024)
- [Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images](https://mlsanity.com/paper/2104.02663/) (2021)
- [EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models](https://mlsanity.com/paper/2410.22959/) (2024)
- [Scalable neural pushbroom architectures for real-time denoising of hyperspectral images onboard satellites](https://mlsanity.com/paper/2601.05020/) (2026)
- [One Model for Two Tasks: Cooperatively Recognizing and Recovering Low-Resolution Scene Text Images by Iterative Mutual Guidance](https://mlsanity.com/paper/2409.14483/) (2024)

## Abstract (arXiv)

By developing sophisticated image priors or designing deep(er) architectures, a variety of image Super-Resolution (SR) approaches have been proposed recently and achieved very promising performance. A natural question that arises is whether these methods can be reformulated into a unifying framework and whether this framework assists in SR reconstruction? In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers). Based on the assumption that better component super-resolver should have larger ensemble weight when performing SR reconstruction, we present a Maximum A Posteriori (MAP) estimation framework for the inference of optimal ensemble weights. Specially, we introduce a reference dataset, which is composed of High-Resolution (HR) and Low-Resolution (LR) image pairs, to measure the super-resolution abilities (prior knowledge) of different component super-resolvers. To obtain the optimal ensemble weights, we propose to incorporate the reconstruction constraint, which states that the degenerated HR image should be equal to the LR observation one, as well as the prior knowledge of ensemble weights into the MAP estimation framework. Moreover, the proposed optimization problem can be solved by an analytical solution. We study the performance of the proposed method by comparing with different competitive approaches, including four state-of-the-art non-deep learning based methods, four latest deep learning based methods and one ensemble learning based method, and prove its effectiveness and superiority on three public datasets.