A new image quality metric for image fusion: The sum of the correlations of differences

By V. Aslantaş, Emre Bendes. 2015

501 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/a177e1a9a11a613b8ce57343971194ba8f191f96

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. Xiaojie Guo
2. Junjun Jiang
3. Han Xu
4. Haibin Ling
5. Zhenfeng Shao
6. Jiayi Ma
7. Hui Li
8. Yang Xiao

In Media Technology

1. Jiayi Ma
2. Hui Li
3. Xiaojun Wu
4. Hao Zhang
5. Josef Kittler
6. Yiannis Demiris
7. Xingchen Zhang
8. Han Xu

Citations in the corpus

• 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.”
• FusionDN: A Unified Densely Connected Network for Image Fusion (2020, methodology): “2004) and sum of the correlations of differences (SCD) (Aslantas and Bendes 2015) are used to evaluate the distortion, noise or artifacts added due to fusion, similarity, and amount of complementary information transferred between the fused image and source images.”
• Infrared and Visible Image Fusion with ResNet and zero-phase component analysis (2018, unknown): “The performance improves with the increasing numerical index of En, MI, Q abf , F MI pixel , F MI dct , F MI w , SCD, SSIM a and MS SSIM.”
• Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation (2023, background): “Four objec-tive metrics are leveraged for the comparison, including entropy (EN)[48], standard deviation (SD)[49], spatial frequency (SF)[50] and the sum of the correlations of differences (SCD)[51].”
• MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion (2018, methodology): “preserved in fused images; the standard deviation (SD) [37]; Qabf [52] reflects the quality of visual information obtained from the fusion of the input images; F M Ip , F M Iw and F M Idct [8] calculate fast mutual information (F M I ), discrete cosine and wavelet features, respectively; SC D [1] ca”
• U2Fusion: A Unified Unsupervised Image Fusion Network (2020, methodology): “Four metrics, namely, correlation coefficient (CC), SSIM, peak signal-to-noise ratio (PSNR), and the sum of the correlations of differences (SCD) [43], are used for evaluation.”
• DenseFuse: A Fusion Approach to Infrared and Visible Images (2018, methodology): “They are: entropy(En); Qabf [28]; the sum of the correlations of differences(SCD) [29]; FMIw and FMIdct [30] which calculates mutual information (FMI) for”
• Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity (2020, methodology): “Quantitative Experiment We still choose EN, SCD, CC, FMI, SD and MI described above to evaluate fused results.”
• 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].”