Information measure for performance of image fusion

By Guihong Qu, Dali Zhang, Pingfan Yan. 2002

1,413 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/fd2ff18f4089588d9853ed47be6d17304a48132a

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Jiayi Ma
2. Hui Li
3. Xiaojun Wu
4. Yu Liu
5. Han Xu
6. Linfeng Tang
7. Hao Zhang
8. Zengfu Wang

In Media Technology

1. Jiayi Ma
2. Hui Li
3. Yu Liu
4. Zengfu Wang
5. Xiaojun Wu
6. Han Xu
7. Hao Zhang
8. Linfeng Tang

Citations in the corpus

• Perceptual Quality Assessment for Multi-Exposure Image Fusion (2015, background): “Over the past decade, substantial effort has been made to develop objective IQA models for image fusion applications [18]–[26].”
• A non-reference image fusion metric based on mutual information of image features (2011, methodology): “To assess the performance of our quality metric, the results are compared with some state-of-the-art metrics such as histogram based MI [30], Piella [34], and Petrovic [35].”
• A general framework for multiresolution image fusion: from pixels to regions (2003, background): “Another non-reference objective performance metric is proposed by Qu et al. [ 71 ].”
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection (2022, background): “Besides, three evaluation metrics, i.e. , mutual information (MI) [30], entropy (EN) [31] and standard deviation (SD) [1] are introduced for evaluation.”
• Image Fusion With Guided Filtering (2013, background): “One problem with traditional mutual information metric [28] is that it is unstable and may bias the measure towards the source image with the highest entropy.”
• Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study (2012, background): “represent the difference in quality [4].”
• GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion (2021, methodology): “In this work, six metrics are used to achieve this goal, which are structural similarity index measure (SSIM) [48], correlation coefficient (CC) [49], sum of the correlations of differences (SCD) [50], entropy (EN) [51], standard deviation (SD) [52], and mutual information (MI) [53].”
• A novel similarity based quality metric for image fusion (2008, methodology): “evaluation results of the proposed method, the MI method in [3] and the Qw method in [5] for the image set illustrated in Fig.”
• LLVIP: A Visible-infrared Paired Dataset for Low-light Vision (2021, methodology): “MI [16] is the most commonly used objective metric for image fusion.”
• Multifocus image fusion using region segmentation and spatial frequency (2008, background): “The second criterion is mutual information (MI) [27].”