Performance comparison of different multi-resolution transforms for image fusion
By Shutao Li, Bin Yang, Jianwen Hu. Information Fusion, 2011
597 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/b6372131befe60eb0594ce0371c66d87d181d442
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In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion (2021, methodology): “Typical traditional methods are sparse representation-based methods [7], [8], multiscale transform-based methods [9], [10], subspace-based methods [11], saliency-based method [12], [13], and hybrid methods [14], [15].”
- Simultaneous image fusion and denoising with adaptive sparse representation (2015, background): “All the above parameters are set according to the results in a survey paper [32], which fully investigates the optimal parameter settings for various multi-scale transform-based fusion methods.”
- Pixel-level image fusion: A survey of the state of the art (2017, methodology): “…decompositions[27– 29], anisotropic heat diffusion[30], log-Gabor transform[31], support value transform [32, 33], optimal scale transform [34, 35] coefficient [4, 17], window[18], and region[13] based activity level measurement, choose-max[28], optimization based method[36],…”
- An overview of multi-modal medical image fusion (2016, background): “QAB/F [63-66] measures the success of edge information transferred from the input images to the fused image.”
- FusionGAN: A generative adversarial network for infrared and visible image fusion (2019, methodology): “They can be simply divided into seven categories including multi-scale transform- [5, 6, 7], sparse representation- [8, 9], neural network- [10, 11], subspace- [12, 13], and saliency-based [14, 15] methods, hybrid models [16, 17], and other methods [18, 19].”
- A general framework for image fusion based on multi-scale transform and sparse representation (2015, methodology): “First, according to [11], the impact of image filters on the fused results is generally much weaker than that of either transform domain or decomposition level.”
- A review of remote sensing image fusion methods (2016, methodology): “The various multi-resolution decomposition algorithms such as curvelet and contourlet are compared in [67] and the effects of decomposition levels and filters on fusion performance are investigated.”