Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study

By Zheng Liu, Erik Blasch, Z. Xue, Jiying Zhao, Robert Laganière, Wei Wu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012

672 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/e684d9f261c586f2544a6a7aab8720e240a04ce3

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. Y. Liu
2. Fazhi He
3. Wei Tang
4. Yu Liu
5. Xun Chen
6. Zengfu Wang
7. Haitao Yin
8. Leyuan Fang

In Media Technology

1. Hui Li
2. Jiayi Ma
3. Yu Liu
4. Xiaojun Wu
5. Xun Chen
6. Zengfu Wang
7. Wei Tang
8. Y. Liu

Citations in the corpus

• YDTR: Infrared and Visible Image Fusion via Y-Shape Dynamic Transformer (2023, background): “As reported in [59], image fusion evaluation metrics can be classified into four categories: information theory-based metrics, image feature-based metrics, image structural similarity-based metrics, and human perception inspired metrics.”
• Pixel-level image fusion: A survey of the state of the art (2017, background): “conduct a comparative study on different image fusion metrics for night vision image fusion applications [113].”
• Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion (2012, neutral): “The chosen five fusion metrics are widely used in fusion evaluation [37], which can assess the quality of fused results reasonably in terms of information transformation, structural similarity, and feature transformation.”
• Simultaneous image fusion and denoising with adaptive sparse representation (2015, methodology): “According to a recent survey by Liu et al. [35], the fusion metrics can be classi fi ed into four categories: information theory-based metrics, image feature-based metrics, image structural similarity-based metrics and human perception-based metrics.”
• Perceptual Quality Assessment for Multi-Exposure Image Fusion (2015, methodology): “Liu for providing the codes of [29], Dr.”
• Multi-focus image fusion with a deep convolutional neural network (2017, methodology): “A good survey provided by Liu et al. [59] classifies them into four groups: information theory-based ones, image feature-based ones, image structural similarity-based ones and human perception-based ones.”
• U2Fusion: A Unified Unsupervised Image Fusion Network (2020, neutral): “[3], [4].”
• Visible and Infrared Image Fusion Using Deep Learning (2023, background): “More details about evaluation metrics can be found in [19], [123], [191].”
• Image Fusion With Guided Filtering (2013, background): “A good survey and comparative study of these quality metrics can be found in Z. Liu et al. ’s work [27].”
• A general framework for image fusion based on multi-scale transform and sparse representation (2015, methodology): “The code of Q P is obtained from a fusion evaluation toolbox [28] provided by Liu (the third author of [24]).”