Comments on 'Information measure for performance of image fusion'
By M. Hossny, Saeid Nahavandi, Douglas C. Creighton. 2008
455 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Media Technology
View Paper: https://www.semanticscholar.org/paper/f7ea879f7e7e08fee0624b50c7fa79ee4491d967
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Citations in the corpus
- YDTR: Infrared and Visible Image Fusion via Y-Shape Dynamic Transformer (2023, background): “…five widely applied evaluation metrics are deployed for comprehensive quantitative comparison, which include the normalized mutual information Q MI [46], nonlinear correlation information entropy Q NCIE [47], phase congruency based feature-wise metric Q P [48], multi-scale structural similarity…”
- Perceptual Quality Assessment for Multi-Exposure Image Fusion (2015, unknown): “First, models based on entropy computations of pixel intensity values and transform coefficients [33], [34] have poor correlation with perceptual quality.”
- A non-reference image fusion metric based on mutual information of image features (2011, methodology): “[43] have adopted this fact to normalize the histogram based MI as: NMI 1⁄4 2 IFA HF þ HA þ IFB HF þ HB ð41Þ where HA, HB, and HF are the histogram based entropies of the images A, B, and F.”
- Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, unknown): “A more detailed explanation of the computation of these coefficients is found at Sheikh and Bovik [276] .”
- Pixel-level image fusion: A survey of the state of the art (2017, background): “propose a normalized version of the MI metric, which also considers the entropy difference between different source images in the evaluation process [100].”
- DATFuse: Infrared and Visible Image Fusion via Dual Attention Transformer (2023, methodology): “…objective evaluation, six frequently-utilized quantitative evaluation indexes for image fusion are utilized: normalized mutual information Q MI [74], Tsallis entropy Q T E [75], nonlinear correlation information entropy Q NCIE [76], phase congruency based image feature metric Q P [77],…”
- Multi-focus image fusion with a deep convolutional neural network (2017, background): “The four selected metrics are: 1) Normalized mutual information Q MI [60] , which measures the amount of mutual information between fused image and source images.”
- Simultaneous image fusion and denoising with adaptive sparse representation (2015, background): “(1) Normalised mutual information (MI) Q MI [36].”
- Image Fusion With Guided Filtering (2013, background): “1) Normalized mutual information QMI [22] is an information theory based metric.”
- Visible and Infrared Image Fusion Using Deep Learning (2023, background): “Many evaluation metrics, such as cross entropy (CE) [187], spatial frequency (SF) [188], and normalized mutual information (NMI) [189], have been proposed.”
- MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion (2018, background): “These are: entropy(En) [38]; mutual information(M I ) [10] indicates how much features are”
- Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study (2012, background): “This may cause instability of the measure and bias the measure toward the source image with the highest entropy [14].”
- Image matting for fusion of multi-focus images in dynamic scenes (2013, methodology): “In this paper, the modified mutual information M F [32], the structural similarity metric SSIM [33], the similarity based quality metric Q(X, Y, F) [34] and the gradient based metric Q [35] are adopted.”
- An overview of multi-modal medical image fusion (2016, background): “MI [58] mainly concentrates on estimating the amount of information transferred from the input images into the fused image”