# Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution

arXiv: https://arxiv.org/abs/1905.02462 · HTML: https://mlsanity.com/paper/1905.02462/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 2 of 2)

- [EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models](https://mlsanity.com/paper/2410.22959/) (2024)
- [Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning](https://mlsanity.com/paper/2404.15704/) (2024)

## Abstract (arXiv)

Recently, image super-resolution has been widely studied and achieved significant progress by leveraging the power of deep convolutional neural networks. However, there has been limited advancement in video super-resolution (VSR) due to the complex temporal patterns in videos. In this paper, we investigate how to adapt state-of-the-art methods of image super-resolution for video super-resolution. The proposed adapting method is straightforward. The information among successive frames is well exploited, while the overhead on the original image super-resolution method is negligible. Furthermore, we propose a learning-based method to ensemble the outputs from multiple super-resolution models. Our methods show superior performance and rank second in the NTIRE2019 Video Super-Resolution Challenge Track 1.