A bio-inspired two-scale complementarity evaluation method for image fusion

Hu, Yanxiang; Zhang, Bo

doi:10.1142/s0219691317500539

Cited by 3 publications

(1 citation statement)

References 29 publications

(39 reference statements)

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“…This metric evaluates fusion quality with the aid of MST and block-wise visual information fidelity analysis. In addition, structural SIMilarity index (SSIM)-based metrics [21], and multi-scale similarity metric [22] have also been proposed. Table 1 outlines some objective evaluation metrics commonly used.…”

Section: Comments On Fusion Quality Evaluationmentioning

confidence: 99%

Multi‐focus image fusion evaluation based on jointly sparse representation and atom focus measure

Zhang

et al. 2020

IET Image Processing

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Multi-focus image fusion (MFIF) tries to combine images with different in-focus regions and get a composite image that is in focus everywhere. Although many new MFIF algorithms based on various new representation models have been proposed in recent years, the performance evaluation of MFIF is still a challenging issue. In this study, a novel MFIF objective evaluation metric based on jointly sparse representation and atom focus measure is proposed. It not only provides a more reliable alternative for MFIF quality measurement but also supplies a unique MFIF performance analysis method at the same time. In the measurement, the sources and their fusion results are decomposed jointly sparse with an over-complete learning dictionary to extract the atom remnants of the source images. Meanwhile, in order to emphasise the fusion effect of in-focus atoms, the sum-modified-Laplacian model is used to measure the atom focus degree. Then, the atom remnants weighted by their focus measures are used to measure MFIF quality. In the experiments, nine recently proposed fusion algorithms were tested to contrast the proposed metrics with other four widely used objective metrics. The experimental results demonstrated the rationality and accuracy of our method. Moreover, it was also proved quantificationally that the fusion degree of atoms is directly related to their in-focus degree, the high in-focus degree atoms usually have poor fusion effect. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Comments On Fusion Quality Evaluationmentioning

confidence: 99%

Multi‐focus image fusion evaluation based on jointly sparse representation and atom focus measure

Zhang

et al. 2020

IET Image Processing

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Hyperspectral Image Fusion Algorithm Based on Improved Deep Residual Network

Zhang

et al. 2023

Signal Processing

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Structural similarity preserving GAN for infrared and visible image fusion

Zhang

Zhou

Zhao

et al. 2020

Int. J. Wavelets Multiresolut Inf. Process.

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Compared with a single image, in a complex environment, image fusion can utilize the complementary information provided by multiple sensors to significantly improve the image clarity and the information, more accurate, reliable, comprehensive access to target and scene information. It is widely used in military and civil fields, such as remote sensing, medicine, security and other fields. In this paper, we propose an end-to-end fusion framework based on structural similarity preserving GAN (SSP-GAN) to learn a mapping of the fusion tasks for visible and infrared images. Specifically, on the one hand, for making the fusion image natural and conforming to visual habits, structure similarity is introduced to guide the generator network produce abundant texture structure information. On the other hand, to fully take advantage of shallow detail information and deep semantic information for achieving feature reuse, we redesign the network architecture of multi-modal image fusion meticulously. Finally, a wide range of experiments on real infrared and visible TNO dataset and RoadScene dataset prove the superior performance of the proposed approach in terms of accuracy and visual. In particular, compared with the best results of other seven algorithms, our model has improved entropy, edge information transfer factor, multi-scale structural similarity and other evaluation metrics, respectively, by 3.05%, 2.4% and 0.7% on TNO dataset. And our model has also improved by 0.7%, 2.82% and 1.1% on RoadScene dataset.

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A bio-inspired two-scale complementarity evaluation method for image fusion

Cited by 3 publications

References 29 publications

Multi‐focus image fusion evaluation based on jointly sparse representation and atom focus measure

Multi‐focus image fusion evaluation based on jointly sparse representation and atom focus measure

Hyperspectral Image Fusion Algorithm Based on Improved Deep Residual Network

Structural similarity preserving GAN for infrared and visible image fusion

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