Two-scale image fusion of visible and infrared images using saliency detection

By D. P. Bavirisetti, R. Dhuli. 2016

436 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/bc4e3945e376509d57bcd4e4929785d024aab4ee

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Jiayi Ma
2. Junjun Jiang
3. Guobao Xiao
4. Linfeng Tang
5. Hui Li
6. Josef Kittler
7. Hao Zhang
8. Xiaojun Wu

In Computer Vision and Pattern Recognition

1. Hui Li
2. Jiayi Ma
3. Xiaojun Wu
4. Linfeng Tang
5. Josef Kittler
6. Hao Zhang
7. Jiangshe Zhang
8. Zixiang Zhao

In Media Technology

1. Hui Li
2. Jiayi Ma
3. Xiaojun Wu
4. Linfeng Tang
5. Josef Kittler
6. Hao Zhang
7. Jiangshe Zhang
8. Zixiang Zhao

Citations in the corpus

• Infrared and Visible Image Fusion with ResNet and zero-phase component analysis (2018, methodology): “In 2016, a two-scale decomposition and saliency detection-based fusion method was proposed by Bavirisetti et al.[6].”
• Infrared and Visible Image Fusion using a Deep Learning Framework (2018, methodology): “[6] proposed a two-scale decomposition and saliency detection-based fusion method, where by the mean and median filter are used to extract the base layers and detail layers.”
• STDFusionNet: An Infrared and Visible Image Fusion Network Based on Salient Target Detection (2021, background): “…and visible image fusion algorithms have been proposed, which could be broadly classified into five classes, including multiscale decomposition-based methods [2], [3], saliency-based methods [4], sparse representation-based methods [5], optimization-based methods [6], and hybrid-based meth-ods [7].”
• FusionGAN: A generative adversarial network for infrared and visible image fusion (2019, background): “M A 9 end [22], ratio of low-pass pyramid (LPP) [3], two-scale image fusion based on visual saliency (TSIFVS) [40], and gradient transfer fusion (GTF) [18].”
• DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion (2020, result): “…compare it with other SOTA models, including FusionGAN [Ma et al. , 2019], Densefuse [Li and Wu, 2018], ImageFuse [Li et al. , 2018], DeepFuse [Prab-hakar et al. , 2017], TSIFVS [Bavirisetti and Dhuli, 2016], TVADMM [Guo et al. , 2017], CSR [Liu et al. , 2016] and ADF [Bavirisetti and Dhuli, 2015].”