An Adaptive Fusion Algorithm for Visible and Infrared Videos Based on Entropy and the Cumulative Distribution of Gray Levels

Hu, Hai‐Miao; Wu, Jiawei; Li, Bo; Guo, Qiang; Zheng, Jiangbin

doi:10.1109/tmm.2017.2711422

Cited by 76 publications

(24 citation statements)

References 27 publications

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“…However, there exist strong dependencies between IID coefficients of the intra-scale and inter-scale. In the future, we will design an adaptive fusion rule to make full use of the dependencies of the IID coefficients [56]. 2The objective image fusion quality metrics can only measure the fused images from a limited perspective.…”

Section: Resultsmentioning

confidence: 99%

Intrinsic Image Decomposition-Based Grey and Pseudo-Color Medical Image Fusion

Heliang

2019

IEEE Access

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It is difficult to extract both structural and functional information from the input grey magnetic resonance imaging (MRI) and pseudo-color positron emission tomography (PET) images using the same decomposition scheme in multi-scale transform fusion methods. To overcome this limitation, we propose two algorithms based on intrinsic image decomposition to decompose MRI and PET images into its two separate components in the spatial domain. Algorithm 1 could extract structural information while reducing the noise from the MRI image. Algorithm 2 is for averaging the color information from the PET image. As for the image fusion rule, the defined importance of image coefficients is used to combine the decomposed two-scale components to produce the final fused image, which could keep more spatial resolution with substitution strategies. It demonstrates that the proposed fusion methods could improve the values of mutual information by the metrics on the disease database. Furthermore, the proposed methods produce the competitive visual signal-to-noise ratio values on experiments for robustness database. In addition to the variance in metrics values, the non-parametric Friedman test and the post-hoc Bonferroni-Dunn test are used to analyze the significant difference between the proposed and the state-of-the-arts methods. INDEX TERMS Two-scale MRI-PET fusion, intrinsic image decomposition, structural and functional information, statistical significance test.

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Section: Resultsmentioning

confidence: 99%

Intrinsic Image Decomposition-Based Grey and Pseudo-Color Medical Image Fusion

Heliang

2019

IEEE Access

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“…The first two metrics, i.e., standard deviation (

) [ 47 ] and entropy (

) [ 48 ], are only dependent on the fused image. The three remaining metrics, i.e.…”

Section: Resultsmentioning

confidence: 99%

Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model

et al. 2018

Sensors

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In infrared and visible image fusion, existing methods typically have a prerequisite that the source images share the same resolution. However, due to limitations of hardware devices and application environments, infrared images constantly suffer from markedly lower resolution compared with the corresponding visible images. In this case, current fusion methods inevitably cause texture information loss in visible images or blur thermal radiation information in infrared images. Moreover, the principle of existing fusion rules typically focuses on preserving texture details in source images, which may be inappropriate for fusing infrared thermal radiation information because it is characterized by pixel intensities, possibly neglecting the prominence of targets in fused images. Faced with such difficulties and challenges, we propose a novel method to fuse infrared and visible images of different resolutions and generate high-resolution resulting images to obtain clear and accurate fused images. Specifically, the fusion problem is formulated as a total variation (TV) minimization problem. The data fidelity term constrains the pixel intensity similarity of the downsampled fused image with respect to the infrared image, and the regularization term compels the gradient similarity of the fused image with respect to the visible image. The fast iterative shrinkage-thresholding algorithm (FISTA) framework is applied to improve the convergence rate. Our resulting fused images are similar to super-resolved infrared images, which are sharpened by the texture information from visible images. Advantages and innovations of our method are demonstrated by the qualitative and quantitative comparisons with six state-of-the-art methods on publicly available datasets.

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“…Improved results were obtained by employing physical-based models, which analyzed the image degradation mechanism of an image by building models to realize scene reconstruction. Researchers have proposed many dehazing algorithms using multiple images or depth information [13]- [17]. For example, polarization-based dehazing methods [13]- [15] use images taken with different degrees of polarization to obtain haze-free images effectively.…”

Section: Introductionmentioning

confidence: 99%

Fast Single-Image Dehazing Algorithm Based on Piecewise Transformation in Optical Model

Wang

Chen

et al. 2021

IEEE Photonics J.

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Single-image dehazing techniques are extensively used in outdoor optical image acquisition equipment. Most existing methods pay attention to use various priors to estimate scene transmission. In this paper, a fast single-image dehazing algorithm is proposed based on a piecewise transformation model between the minimum channels of the hazy image and the haze-free image in optical model. The minimum channel of the haze-free image is obtained by the piecewise transformation, which is a quadratic function model that we establish for the dark region, and a linear transformation model is established for the bright region. Using the minimum channels of the hazy image and the haze-free image, a transmission estimation model is established based on the haze optical model with adjustment variables. To obtain an accurate estimation of atmospheric light, we estimate the atmospheric light twice. Finally, the haze-free image is restored. Experimental results show that the proposed algorithm has minimal halo artifacts and color distortion in various depths of field, flat areas and sky areas. From the subjective evaluation, objective evaluation and running time analysis, it can be seen that the algorithm in this paper is superior to most existing technologies.

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An Adaptive Fusion Algorithm for Visible and Infrared Videos Based on Entropy and the Cumulative Distribution of Gray Levels

Cited by 76 publications

References 27 publications

Intrinsic Image Decomposition-Based Grey and Pseudo-Color Medical Image Fusion

Intrinsic Image Decomposition-Based Grey and Pseudo-Color Medical Image Fusion

Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model

Fast Single-Image Dehazing Algorithm Based on Piecewise Transformation in Optical Model

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