# Deep Learning for Multiple-Image Super-Resolution

arXiv: https://arxiv.org/abs/1903.00440 · HTML: https://mlsanity.com/paper/1903.00440/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2019.

## Built on by (most cited first, 15 of 15)

- [MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding](https://mlsanity.com/paper/2406.09411/) (2024)
- [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://mlsanity.com/paper/2102.09351/) (2021)
- [Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution](https://mlsanity.com/paper/2204.04218/) (2022)
- [MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution](https://mlsanity.com/paper/2210.02745/) (2022)
- [Image Fusion in Remote Sensing: An Overview and Meta Analysis](https://mlsanity.com/paper/2401.08837/) (2024)
- [Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images](https://mlsanity.com/paper/2301.11154/) (2023)
- [Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series](https://mlsanity.com/paper/2208.02349/) (2022)
- [SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model](https://mlsanity.com/paper/2503.14463/) (2025)
- [Closed-loop Feedback Registration for Consecutive Images of Moving Flexible Targets](https://mlsanity.com/paper/2110.10772/) (2021)
- [Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective](https://mlsanity.com/paper/2303.03212/) (2023)
- [CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution](https://mlsanity.com/paper/2303.06548/) (2023)
- [CANDID: Correspondence AligNment for Deep-burst Image Denoising](https://mlsanity.com/paper/2306.09887/) (2023)
- [A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method](https://mlsanity.com/paper/2412.09846/) (2024)
- [Burst Image Super-Resolution via Multi-Cross Attention Encoding and Multi-Scan State-Space Decoding](https://mlsanity.com/paper/2505.19668/) (2025)
- [Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation](https://mlsanity.com/paper/2605.02471/) (2026)

## Abstract (arXiv)

Super-resolution reconstruction (SRR) is a process aimed at enhancing spatial resolution of images, either from a single observation, based on the learned relation between low and high resolution, or from multiple images presenting the same scene. SRR is particularly valuable, if it is infeasible to acquire images at desired resolution, but many images of the same scene are available at lower resolution---this is inherent to a variety of remote sensing scenarios. Recently, we have witnessed substantial improvement in single-image SRR attributed to the use of deep neural networks for learning the relation between low and high resolution. Importantly, deep learning has not been exploited for multiple-image SRR, which benefits from information fusion and in general allows for achieving higher reconstruction accuracy. In this letter, we introduce a new method which combines the advantages of multiple-image fusion with learning the low-to-high resolution mapping using deep networks. The reported experimental results indicate that our algorithm outperforms the state-of-the-art SRR methods, including these that operate from a single image, as well as those that perform multiple-image fusion.