Sensor noise effects on signal-level image fusion performance

Petrović, Vladimir; Xydeas, C.S.

doi:10.1016/s1566-2535(03)00035-6

Cited by 97 publications

(73 citation statements)

References 20 publications

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“…The performance evaluation criteria of image fusion are still a hot topic in the research of image fusion, besides visual observation, mutual information(MI) [25], and / AB F Q [26] are used as information-based objective criteria. The reason is that image fusion aims at combining information and these criteria do not require the information of ideal fused image.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Image Fusion Based on Joint Nonsubsampled Contourlet and Overcomplete Brushlet Transforms

Zhang

2016

International Journal on Smart Sensing and Intelligent Systems

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Section: Experimental Results and Analysismentioning

confidence: 99%

Image Fusion Based on Joint Nonsubsampled Contourlet and Overcomplete Brushlet Transforms

Zhang

2016

International Journal on Smart Sensing and Intelligent Systems

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“…The proposed quality measure is computed using the superresolved face image and the input face images in each case. We choose the size of the sliding window as 8 × 8 and θ as the inverse of the number of the input images in Equation (10). Moveover, we compute the UQI and PSNR between the super-resolved image and the reference image for comparison.…”

Section: Methodsmentioning

confidence: 99%

Evaluating the quality of super-resolved images for face recognition

Zhou

Bhanu

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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The widespread use of super-resolution methods, in a variety of applications such as surveillance has led to an increasing need for or quality assessment measures. The current quality measures aim to compare different fusion methods by assessing the quality of the fused images. They consider the information transferred between the super-resolved image and input images only. In this paper, we propose an objective quality evaluation algorithm for super-resolved images, which focuses on evaluating the quality of super-resolved images that are constructed from different conditions of input images. The proposed quality evaluation method combines both the relationship between the super-resolved image and the input images, and the relationship between the input images. Using the proposed measure, the quality of the super-resolved face images constructed from videos are evaluated under different conditions, including the variation of pose, lighting, facial expressions and the number of input images.

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“…There will be are duction in noise factor in the fused image and the reliability factor will increase providing a high-quality image [17]. v. The multi-sensor fusion system provides redundancy in numerous quantities that can support in structures strength [18]. In a circumstance when one or additional sensors stop working or the concert of a specific sensor fails, the method can rely on the additional sensors within the system [19].…”

Section: Benefitsmentioning

confidence: 99%

Image Fusion Methods: A Survey

Masood¹,

Sharif²,

Yasmin³

et al. 2017

JESTR

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Image fusion is an approach which is used to amalgamate the corresponding features in a sequence of input images to a single composite image that preserves all the significant features of the input images. Image fusion is also known as pansharpening. It is a method which is used to integrate and add the geometric detail of a high-resolution panchromatic (Pan) image and the information of color of a low-resolution multispectral (MS) image for the production of a high-resolution MS image. This methodology is mainly most important and significant for any large-scale applications. Image fusion classification based on its systems (models and algorithms) are considered and overviewed in this survey. The basic two algorithms categories are analyzed and compared to each other, in the analysis and discussion section the major points to be considered while performing image fusion are highlighted.

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Sensor noise effects on signal-level image fusion performance

Cited by 97 publications

References 20 publications

Image Fusion Based on Joint Nonsubsampled Contourlet and Overcomplete Brushlet Transforms

Image Fusion Based on Joint Nonsubsampled Contourlet and Overcomplete Brushlet Transforms

Evaluating the quality of super-resolved images for face recognition

Image Fusion Methods: A Survey

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