Visible and Near Infrared Image Fusion Using Base Tone Compression and Detail Transform Fusion

Son, Dong-Min; Kwon, Hyunwook; Lee, Sung-Hak

doi:10.3390/chemosensors10040124

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…• Color not smoothly dispersed in spatial features • Edges of small objects are lost Intensity hue saturation (HIS) method [11] • Enhanced fusion used for desperate datasets • Converts color images into its RGB of HIS spectrum…”

Section: Methods Advantages Disadvantagesmentioning

confidence: 99%

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A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion

Ignatious,

El-Sayed,

Khan

et al. 2023

Applied Sciences

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Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today’s technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focused on data fusion. In particular, this study proposed a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposed a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model was proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features were also proposed. The hybrid image fusion model that was proposed did not yield satisfactory outcomes when combining 3D point cloud data, and its performance declined when evaluating large datasets. To address this issue, the study expanded by introducing an advanced generative adversarial network (GAN) to transform the hybrid image fusion model into a machine learning model capable of handling substantial datasets. Additionally, customized kernel functions were suggested to fuse 3D point cloud data effectively. The performance of the proposed models was assessed using standard metrics and datasets, comparing them with existing popular models. The results revealed that the proposed image fusion model outperformed the other models.

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Section: Methods Advantages Disadvantagesmentioning

confidence: 99%

“…• Limited bands • Difficult to fuse multispectral bands Principe component analysis [11] • Redundancy of data is decreased…”

Section: Methods Advantages Disadvantagesmentioning

confidence: 99%

A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion

Ignatious,

El-Sayed,

Khan

et al. 2023

Applied Sciences

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“…Infrared and visible image registration is a multimodal image registration problem [22,23], since infrared images and visible images provide complementary information, respectively. Therefore, these two kinds of images are heavily used in vision applications (image fusion and image stitching) [24][25][26][27]. However, due to the difference in band and resolution between infrared and visible sensors, the two images cannot be directly fused.…”

Section: Related Workmentioning

confidence: 99%

RI-MFM: A Novel Infrared and Visible Image Registration with Rotation Invariance and Multilevel Feature Matching

Zhu

Zhan

et al. 2022

Electronics

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In the past ten years, multimodal image registration technology has been continuously developed, and a large number of researchers have paid attention to the problem of infrared and visible image registration. Due to the differences in grayscale distribution, resolution and viewpoint between two images, most of the existing infrared and visible image registration methods are still insufficient in accuracy. To solve such problems, we propose a new robust and accurate infrared and visible image registration method. For the purpose of generating more robust feature descriptors, we propose to generate feature descriptors using a concentric-circle-based feature-description algorithm. The method enhances the description of the main direction of feature points by introducing centroids, and, at the same time, uses concentric circles to ensure the rotation invariance of feature descriptors. To match feature points quickly and accurately, we propose a multi-level feature-matching algorithm using improved offset consistency for matching feature points. We redesigned the matching algorithm based on the offset consistency principle. The comparison experiments with several other state-of-the-art registration methods in CVC and homemade datasets show that our proposed method has significant advantages in both feature-point localization accuracy and correct matching rate.

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“…The Gaussian pyramid is created by applying a Gaussian filter to an image to create a blurred image and then down-sampling the image to which the filter is applied. Then, the down-sampled image is up-sampled and subtracted from the image before down-sampling to generate the Laplacian pyramid image [ 17 ]. Thus generated Laplacian pyramid stores the differential images obtained by the Gaussian pyramid.…”

Section: Related Workmentioning

confidence: 99%

Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion

Son

Kwon

et al. 2022

Entropy

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In this paper, to improve the slow processing speed of the rule-based visible and NIR (near-infrared) image synthesis method, we present a fast image fusion method using DenseFuse, one of the CNN (convolutional neural network)-based image synthesis methods. The proposed method applies a raster scan algorithm to secure visible and NIR datasets for effective learning and presents a dataset classification method using luminance and variance. Additionally, in this paper, a method for synthesizing a feature map in a fusion layer is presented and compared with the method for synthesizing a feature map in other fusion layers. The proposed method learns the superior image quality of the rule-based image synthesis method and shows a clear synthesized image with better visibility than other existing learning-based image synthesis methods. Compared with the rule-based image synthesis method used as the target image, the proposed method has an advantage in processing speed by reducing the processing time to three times or more.

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Visible and Near Infrared Image Fusion Using Base Tone Compression and Detail Transform Fusion

Cited by 9 publications

References 27 publications

A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion

A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion

RI-MFM: A Novel Infrared and Visible Image Registration with Rotation Invariance and Multilevel Feature Matching

Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion

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