Three-Layer Image Representation by an Enhanced Illumination-Based Image Fusion Method

Du, Jiao; Li, Weisheng; Tan, Hengliang

doi:10.1109/jbhi.2019.2930978

Cited by 30 publications

(12 citation statements)

References 47 publications

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“…The energy calculation formula is shown in equation ( 2). Du et al [7]. proposed a method based on three-layer image decomposition and enhanced lighting fusion rules, in which each input image is decomposed into corresponding smooth layer, texture layer and edge layer using defined local extremum and low-pass filter in the spatial domain.…”

Section: Image Fusionmentioning

confidence: 99%

Unordered Image Stitching Based on the Registration Index Table

2022

J. Phys.: Conf. Ser.

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In order to solve the problem of large amount of calculation and time-consuming in the process of unordered image stitching, we propose an image stitching algorithm based on the image registration index table in this paper. Firstly, we construct the matching index sequence based on the similarity of the matching area. Then we search for image matching pairs according to the matching index sequence, carry out image registration and stitch directly, and generate the registration index table. For image stitching, we build a binary tree model assisted by the image registration index table, we need to search for matching pairs and carry out registration and stitching according to the registration index table generated by each layer. Compare to the current research on disordered image stitching, our method significantly reduces the times of feature extraction and matching by at least half, therefore, it greatly improves the efficiency of image stitching.

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Section: Image Fusionmentioning

confidence: 99%

Unordered Image Stitching Based on the Registration Index Table

2022

J. Phys.: Conf. Ser.

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“…In addition to spatial domain-based methods and transform domain-based methods, extensive work has also been conducted with soft computing-based methods dedicated to multimodal medical image fusion. A great many representative models, including dictionary learning model (Zhu et al, 2016 ; Li et al, 2018 ), gray wolf optimization (Daniel, 2018 ), fuzzy theory (Yang et al, 2019 ), pulse coupled neural network (Liu X. et al, 2016 ; Xu et al, 2016 ), sparse representation (Liu and Wang, 2015 ; Liu Y. et al, 2016 ), total variation (Zhao and Lu, 2017 ), guided filter (Li et al, 2019 ; Zhang et al, 2021 ), genetic algorithm (Kavitha and Thyagharajan, 2017 ; Arif and Wang, 2020 ), compressed sensing (Ding et al, 2019 ), structure tensor (Du et al, 2020c ), local extrema (Du et al, 2020b ), Otsu's method (Du et al, 2020a ) and so on, were successfully used to fuse the medical images.…”

Section: Introductionmentioning

confidence: 99%

Multimodal medical image fusion using convolutional neural network and extreme learning machine

Kong

Lei³

2022

Front. Neurorobot.

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The emergence of multimodal medical imaging technology greatly increases the accuracy of clinical diagnosis and etiological analysis. Nevertheless, each medical imaging modal unavoidably has its own limitations, so the fusion of multimodal medical images may become an effective solution. In this paper, a novel fusion method on the multimodal medical images exploiting convolutional neural network (CNN) and extreme learning machine (ELM) is proposed. As a typical representative in deep learning, CNN has been gaining more and more popularity in the field of image processing. However, CNN often suffers from several drawbacks, such as high computational costs and intensive human interventions. To this end, the model of convolutional extreme learning machine (CELM) is constructed by incorporating ELM into the traditional CNN model. CELM serves as an important tool to extract and capture the features of the source images from a variety of different angles. The final fused image can be obtained by integrating the significant features together. Experimental results indicate that, the proposed method is not only helpful to enhance the accuracy of the lesion detection and localization, but also superior to the current state-of-the-art ones in terms of both subjective visual performance and objective criteria.

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“…The first approach is to segment the local information of medical images and enhance those important for further feature extraction and MIR. In [1], each input image is decomposed into corresponding smooth layer, texture layer and edge layer by using the local extreme value defined in the spatial domain and low-pass filter. In [2], a fuzzy-rough refined image processing framework is proposed to segment the ROI region of each breast image and perform local enhancement in the region with the highest positive fuzzy region.…”

Section: Introductionmentioning

confidence: 99%

A Self-Adaptive Discriminative Autoencoder for Medical Applications

Shang

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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Computer aided diagnosis (CAD) systems play an essential role in the early detection and diagnosis of developing disease for medical applications. In order to obtain the highly recognizable representation for the medical images, a self-adaptive discriminative autoencoder (SADAE) is proposed in this paper. The proposed SADAE system is implemented under a deep metric learning framework which consists of K local autoencoders, employed to learn the K subspaces that represent the diverse distribution of the underlying data, and a global autoencoder to restrict the spatial scale of the learned representation of images. Such community of autoencoders is aided by a self-adaptive metric learning method that extracts the discriminative features to recognize the different categories in the given images. The quality of the extracted features by SADAE is compared against that of those extracted by other state-of-the-art deep learning and metric learning methods on five popular medical image data sets. The experimental results demonstrate that the medical image recognition results gained by SADAE are much improved over those by the alternatives.

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Three-Layer Image Representation by an Enhanced Illumination-Based Image Fusion Method

Cited by 30 publications

References 47 publications

Unordered Image Stitching Based on the Registration Index Table

Unordered Image Stitching Based on the Registration Index Table

Multimodal medical image fusion using convolutional neural network and extreme learning machine

A Self-Adaptive Discriminative Autoencoder for Medical Applications

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