Review of pixel-level image fusion

Yang, Bo; Jing, Zhongliang; Zhao, Haitao

doi:10.1007/s12204-010-7186-y

Cited by 94 publications

(25 citation statements)

References 34 publications

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“…11 This is certainly the case in whole-body transillumination, PET/CR, and PET/MR systems; each system aligns the imagery so that fusion can operate at the pixel level. 13 Several classes of image fusion algorithms are now presented along with an example of each.…”

Section: Brief Overview Of Existing Image Fusion Techniquesmentioning

confidence: 99%

Formulation of image fusion as a constrained least squares optimization problem

et al. 2017

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Fusing a lower resolution color image with a higher resolution monochrome image is a common practice in medical imaging. By incorporating spatial context and/or improving the signal-to-noise ratio, it provides clinicians with a single frame of the most complete information for diagnosis. In this paper, image fusion is formulated as a convex optimization problem that avoids image decomposition and permits operations at the pixel level. This results in a highly efficient and embarrassingly parallelizable algorithm based on widely available robust and simple numerical methods that realizes the fused image as the global minimizer of the convex optimization problem.

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Section: Brief Overview Of Existing Image Fusion Techniquesmentioning

confidence: 99%

Formulation of image fusion as a constrained least squares optimization problem

et al. 2017

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“…Thus, we will be interested with low-level fusion. However, in this fusion, we must satisfy a set of constraints [26]: the fusion process should preserve all relevant information on the input imagery in the composite image; the fusion process should not introduce any artifacts or inconsistencies and the fusion process should be shift and rotational invariant. Moreover, low-level fusion techniques suppose that a perfect correlation between the images is performed before performing the fusion itself.…”

Section: State Of Art On Fusion Techniquesmentioning

confidence: 99%

Intelligent Fusion of Infrared and Visible Spectrum for Video Surveillance Application

Boukhriss

Fendri

Hammami

2015

Artificial Intelligence and Soft Computing

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Abstract. In video surveillance, we can rely on either a visible spectrum or an infrared one. In order to profit from both of them, several fusion methods were proposed in literature: low-level fusion, middle-level fusion and high-level fusion. The first one is the most used for moving objects' detection. It consists in merging information from visible image and infrared one into a new synthetic image to detect objects. However, the fusion process may not preserve all relevant information. In addition, perfect correlation between the two spectrums is needed. In This paper, we propose an intelligent fusion method for moving object detection. The proposed method relies on one of the two given spectrum at once according to weather conditions (darkness, sunny days, fog, snow, etc.). Thus, we first extract a set of low-level features (visibility, local contrast, sharpness, hue, saturation and value), then a prediction model is generated by supervised learning techniques. The classification results on 15 sequences with different weather conditions indicate the effectiveness of the extracted features, by using C4.5 as classifier.

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“…The methods can be generally classified into spatial domain and transform domain based techniques [3]. Several detailed surveys on this issue can be seen from references [4]- [6]. The simplest fusion method is to take the average of the two images pixel by pixel.…”

Section: Introductionmentioning

confidence: 99%

Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

Yang

Huang

Gao

et al. 2014

Measurement Science Review

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In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

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Review of pixel-level image fusion

Cited by 94 publications

References 34 publications

Formulation of image fusion as a constrained least squares optimization problem

Formulation of image fusion as a constrained least squares optimization problem

Intelligent Fusion of Infrared and Visible Spectrum for Video Surveillance Application

Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

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