A new quality metric for image fusion
By Gemma Piella, Henk J. A. M. Heijmans. Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003
937 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/88009329a4b8614afbc478a19d2e85b15c2709ae
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In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “[18,19] the fitness function F fg has been defined as: F fg ¼ ðf ; gÞ kf kkgk”
- Pixel-level image fusion with simultaneous orthogonal matching pursuit (2012, background): “(3) Image quality metric based metrics include the local quality index (Q0), the weighted fusion quality (QW) measure, and the edge dependent fusion quality index (QE) [43,44], which assess the pixel-level fusion performance objectively.”
- Image fusion: Advances in the state of the art (2007, neutral): “In recent years, a number of computational image fusion quality assessment metrics have been proposed [65,66].”
- Optimizing the High-Pass Filter Addition Technique for Image Fusion (2007, background): “A wide variety of metrics are presented in the literature (Wald et al., 1997; Nunez et al., 1999; Piella and Heijmans, 2003; Ranchin et al., 2003; Shi et al., 2003; Zhou et al., 1998).”
- Perceptual Quality Assessment for Multi-Exposure Image Fusion (2015, methodology): “Three implementations of the algorithm in [22] were proposed and the one with the best performance is reported here.”
- 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 Critical Comparison Among Pansharpening Algorithms (2015, background): “The definition of a technique that fulfills the constraints defined in the protocol is still an open problem [6], [37] and relates closely to the general discussion regarding image quality assessment [38] and image fusion [39], [40].”
- Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study (2012, methodology): “1 Piella’s Metric (QS) Piella and Heijmans defined three fusion quality index based on Wang’s UIQI method [5].”
- Infrared and Visible Image Fusion via Decoupling Network (2022, background): “Third, the gradient-based fusion metric ( Q ABF ) that measures the amount of edge information from the source images to the fused image [43].”
- Hyperspectral Pansharpening: A Review (2015, background): “However, the definition of an assessment method that fulfills these constraints is still an open issue [80], [81], and closely relates to the general discussion regarding image quality assessment [82] and image fusion [83], [84].”
- 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.”
- A general framework for image fusion based on multi-scale transform and sparse representation (2015, methodology): “The universal image quality index (UIQI) [25] based fusion metric Q W proposed by Piella and Heijmans [26].”
- A new automated quality assessment algorithm for image fusion (2009, methodology): “Universal index based measure (UI) Piella and Heijmans [9] proposed a new quality metric for image fusion based on research by Wang and Bovik [10] on a structural similarity (SSIM) measure.”