# Visible and Infrared Image Fusion Using Encoder-Decoder Network

arXiv: https://arxiv.org/abs/2412.08073 · HTML: https://mlsanity.com/paper/2412.08073/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [Infrared and Visible Image Fusion using a Deep Learning Framework](https://mlsanity.com/paper/1804.06992/) (2018)
- [VIFB: A Visible and Infrared Image Fusion Benchmark](https://mlsanity.com/paper/2002.03322/) (2020)
- [Infrared and Visible Image Fusion with ResNet and zero-phase component analysis](https://mlsanity.com/paper/1806.07119/) (2018)
- [A Dual-branch Network for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2101.09643/) (2021)

## Built on by (most cited first, 1 of 1)

- [AngularFuse: A Closer Look at Angle-based Perception for Spatial-Sensitive Multi-Modality Image Fusion](https://mlsanity.com/paper/2510.12260/) (2025)

## Abstract (arXiv)

The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we present a novel learning-based solution to image fusion problem focusing on infrared and visible spectrum images. The proposed solution utilizes only convolution and pooling layers together with a loss function using no-reference quality metrics. The analysis is performed qualitatively and quantitatively on various datasets. The results show better performance than state-of-the-art methods. Also, the size of our network enables real-time performance on embedded devices. Project codes can be found at urlhttps://github.com/ferhatcan/pyFusionSR.