A survey on region based image fusion methods

Meher, Bikash; Agrawal, Sanjay; Panda, Rutuparna; Abraham, Ajith

doi:10.1016/j.inffus.2018.07.010

Cited by 199 publications

(91 citation statements)

References 67 publications

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“…A survey of existing saliency-based methods by Meher et al [23] shows that most saliency-based methods are to separately extract targets from the infrared image and then superimpose them into the visible image (accurate extraction of the target contour is often difficult), which not only loses a lot of complementary information in visible image, but also the noise in the visible image will greatly affect the robustness of the fusion algorithm. Different from existing methods, we use the proposed special visual attention system to extract the features of the infrared and visible image, respectively.…”

Section: Feature Fusion Strategy Based On the Saliency Mapsmentioning

confidence: 99%

“…In addition, there are other ideas and perspectives that inspire new image fusion method, such as entropy [21], total variation [22] and so on.With the development of computer vision technology, saliency-based methods have been successfully implemented to infrared and visible image fusion because it effectively utilizes the complementary information of the source image. Saliency-based methods have four fusion steps including saliency region segmentation, designing a fusion rule of saliency region, designing a fusion rule of the background region, and reconstruction [23]. Meng et al [24] used significant detection method to extract the interesting region, which can be mapped to the region of the fused image.…”

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confidence: 99%

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An Efficient Method for Infrared and Visual Images Fusion Based on Visual Attention Technique

et al. 2020

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Infrared and visible image fusion technology provides many benefits for human vision and computer image processing tasks, including enriched useful information and enhanced surveillance capabilities. However, existing fusion algorithms have faced a great challenge to effectively integrate visual features from complex source images. In this paper, we design a novel infrared and visible image fusion algorithm based on visual attention technology, in which a special visual attention system and a feature fusion strategy based on the saliency maps are proposed. Special visual attention system first utilizes the co-occurrence matrix to calculate the image texture complication, which can select a particular modality to compute a saliency map. Moreover, we improved the iterative operator of the original visual attention model (VAM), a fair competition mechanism is designed to ensure that the visual feature in detail regions can be extracted accurately. For the feature fusion strategy, we use the obtained saliency map to combine the visual attention features, and appropriately enhance the tiny features to ensure that the weak targets can be observed. Different from the general fusion algorithm, the proposed algorithm not only preserve the interesting region but also contain rich tiny details, which can improve the visual ability of human and computer. Moreover, experimental results in complicated ambient conditions show that the proposed algorithm in this paper outperforms state-of-the-art algorithms in both qualitative and quantitative evaluations, and this study can extend to the field of other-type image fusion.

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Section: Feature Fusion Strategy Based On the Saliency Mapsmentioning

confidence: 99%

mentioning

confidence: 99%

An Efficient Method for Infrared and Visual Images Fusion Based on Visual Attention Technique

et al. 2020

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“…The purpose of the fusion is to obtain better contrast, fusion quality, and perceived experience. The result of the fusion should meet the following conditions: (a) the fused image should retain the information of source images completely; (b) the fused image should not produce any synthetic information, such as artifacts; and (c) bad states should be avoided, such as misregistration and noise [1].…”

Section: Introductionmentioning

confidence: 99%

A Review of Multimodal Medical Image Fusion Techniques

Huang

Yang

Yin

et al. 2020

Computational and Mathematical Methods in Medicine

119

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The medical image fusion is the process of coalescing multiple images from multiple imaging modalities to obtain a fused image with a large amount of information for increasing the clinical applicability of medical images. In this paper, we attempt to give an overview of multimodal medical image fusion methods, putting emphasis on the most recent advances in the domain based on (1) the current fusion methods, including based on deep learning, (2) imaging modalities of medical image fusion, and (3) performance analysis of medical image fusion on mainly data set. Finally, the conclusion of this paper is that the current multimodal medical image fusion research results are more significant and the development trend is on the rise but with many challenges in the research field.

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“…Also introduced some novel trends in the medical fusion filed. Bikash Meher , Sanjay Agrawal , Rutuparna Panda , Ajith Abraham offered survey paper" A survey on region based image fusion methods " in (2018) [25],they focused on state-of-the-art survey methods for region detection with concluded that ICA perform better outcome fused image in term of region detect ,compression and evaluation with various metrics for different application was reviewed like medical image with brain dataset ,standard images datasets.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Multi-Scale Medical images Fusion Techniques: Brain Diseases

Yousif

Omar

Sheikh

2020

JBEMI

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Brain diseases such as degenerative (alzheimer's disease), neoplastic disease (brain tumor like sarcoma, glioma) are considered an interesting topic areas in the medical image fusion diagnosis. Pixel-level image fusion techniques are designed to combine multiple/multi-scale input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Since they are difficult to be summarized ; survey paper are characterized by (1) medical image definition , brain diseases challenges , analysis a various techniques for multi-scale image fusion with its own modalities, fusion rule, fusion strategy and dis-advantage ,Whilst used a database of medical images for medical Harvard School (brain diseases) which contains various groups of co-registered multi-modal images including MRI/CT, MRI/PET and PET/SPECT and MRI (T1/T2) images.

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A survey on region based image fusion methods

Cited by 199 publications

References 67 publications

An Efficient Method for Infrared and Visual Images Fusion Based on Visual Attention Technique

An Efficient Method for Infrared and Visual Images Fusion Based on Visual Attention Technique

A Review of Multimodal Medical Image Fusion Techniques

A Survey on Multi-Scale Medical images Fusion Techniques: Brain Diseases

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