scholarly journals Infrared and Visible Image Fusion Using Truncated Huber Penalty Function Smoothing and Visual Saliency Based Threshold Optimization

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Chaowei Duan ◽  
Yiliu Liu ◽  
Changda Xing ◽  
Zhisheng Wang
Keyword(s):  
Image Fusion ◽  
Penalty Function ◽  
Visual Saliency ◽  
Fusion Rule ◽  
Visible Image ◽  
Threshold Optimization ◽  
Fused Image ◽  
Fusion Methods ◽  
Visible Images ◽  
Image Representations

An efficient method for the infrared and visible image fusion is presented using truncated Huber penalty function smoothing and visual saliency based threshold optimization. The method merges complementary information from multimodality source images into a more informative composite image in two-scale domain, in which the significant objects/regions are highlighted and rich feature information is preserved. Firstly, source images are decomposed into two-scale image representations, namely, the approximate and residual layers, using truncated Huber penalty function smoothing. Benefiting from the edge- and structure-preserving characteristics, the significant objects and regions in the source images are effectively extracted without halo artifacts around the edges. Secondly, a visual saliency based threshold optimization fusion rule is designed to fuse the approximate layers aiming to highlight the salient targets in infrared images and remain the high-intensity regions in visible images. The sparse representation based fusion rule is adopted to fuse the residual layers with the goal of acquiring rich detail texture information. Finally, combining the fused approximate and residual layers reconstructs the fused image with more natural visual effects. Sufficient experimental results demonstrate that the proposed method can achieve comparable or superior performances compared with several state-of-the-art fusion methods in visual results and objective assessments.

scholarly journals Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

2020 ◽  
Author(s):  
Xiaoxue XING ◽  
Cheng LIU ◽  
Cong LUO ◽  
Tingfa XU
Keyword(s):  
Thermal Radiation ◽  
High Frequency ◽  
Geometric Analysis ◽  
Low Frequency ◽  
Fusion Rule ◽  
Fusion Algorithm ◽  
Visible Image ◽  
Fused Image ◽  
Fusion Methods ◽  
Visible Images

Abstract In Multi-scale Geometric Analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of the images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and Non-Subsampled Shearlet Transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low- and high-frequency sub-bands. Then, the Wavelet Transform (WT) is used to decompose high-frequency sub-bands into obtain approximate sub-bands and directional detail sub-bands. The “average” fusion rule is performed for fusion for approximate sub-bands. And the “max-absolute” fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the high-frequency sub-bands. To highlight the thermal radiation target, we construct a non-linear transform function to determine the fusion weight of low-frequency sub-bands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.

scholarly journals Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

2020 ◽  
Author(s):  
Xiaoxue XING ◽  
Cheng LIU ◽  
Cong LUO ◽  
Tingfa XU
Keyword(s):  
Thermal Radiation ◽  
High Frequency ◽  
Geometric Analysis ◽  
Low Frequency ◽  
Fusion Rule ◽  
Fusion Algorithm ◽  
Visible Image ◽  
Fused Image ◽  
Fusion Methods ◽  
Visible Images

Abstract In Multi-scale Geometric Analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of the images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and Non-Subsampled Shearlet Transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low- and high-frequency sub-bands. Then, the wavelet transform(WT) is used to decompose high-frequency sub-bands into obtain approximate sub-bands and directional detail sub-bands. The “average” fusion rule is performed for fusion for approximate sub-bands. And the “max-absolute” fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the high-frequency sub-bands. To highlight the thermal radiation target, we construct a non-linear transform function to determine the fusion weight of low-frequency sub-bands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.

scholarly journals Fusion of Infrared and Visible Images Using Fast Global Smoothing Decomposition and Target-Enhanced Parallel Gaussian Fuzzy Logic

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 40
Author(s):  
Chaowei Duan ◽  
Changda Xing ◽  
Yiliu Liu ◽  
Zhisheng Wang
Keyword(s):  
Fuzzy Logic ◽  
Image Fusion ◽  
Visual Saliency ◽  
Fusion Rule ◽  
Objective Evaluation ◽  
Saliency Map ◽  
Visible Image ◽  
Structure Information ◽  
Visible Images ◽  
Image Pairs

As a powerful technique to merge complementary information of original images, infrared (IR) and visible image fusion approaches are widely used in surveillance, target detecting, tracking, and biological recognition, etc. In this paper, an efficient IR and visible image fusion method is proposed to simultaneously enhance the significant targets/regions in all source images and preserve rich background details in visible images. The multi-scale representation based on the fast global smoother is firstly used to decompose source images into the base and detail layers, aiming to extract the salient structure information and suppress the halos around the edges. Then, a target-enhanced parallel Gaussian fuzzy logic-based fusion rule is proposed to merge the base layers, which can avoid the brightness loss and highlight significant targets/regions. In addition, the visual saliency map-based fusion rule is designed to merge the detail layers with the purpose of obtaining rich details. Finally, the fused image is reconstructed. Extensive experiments are conducted on 21 image pairs and a Nato-camp sequence (32 image pairs) to verify the effectiveness and superiority of the proposed method. Compared with several state-of-the-art methods, experimental results demonstrate that the proposed method can achieve more competitive or superior performances according to both the visual results and objective evaluation.

scholarly journals Infrared and visible image fusion based on optimal segmenting and contour extraction

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Javad Abbasi Aghamaleki ◽  
Alireza Ghorbani
Keyword(s):  
Image Fusion ◽  
Infrared Image ◽  
Fusion Method ◽  
Visible Image ◽  
Output Image ◽  
Contour Lines ◽  
Fused Image ◽  
Fusion Methods ◽  
Visible Images ◽  
The Individual

AbstractImage fusion is the combining process of complementary information of multiple same scene images into an output image. The resultant output image that is named fused image, produces more precise description of the scene than any of the individual input images. In this paper, we propose a novel simple and fast strategy for infrared (IR) and visible images based on local important areas of IR image. The fusion method is completed in three step approach. Firstly, only the segmented regions in the infrared image is extracted. Next, the image fusion is applied on segmented area and finally, contour lines are also used to improve the quality of the results of the second step of fusion method. Using a publicly available database, the proposed method is evaluated and compared to the other fusion methods. The experimental results show the effectiveness of the proposed method compared to the state of the art methods.

scholarly journals Infrared and Visible Image Fusion through Details Preservation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4556 ◽  
Author(s):  
Yaochen Liu ◽  
Lili Dong ◽  
Yuanyuan Ji ◽  
Wenhai Xu
Keyword(s):  
Image Fusion ◽  
Subjective Evaluation ◽  
Objective Assessment ◽  
Decomposition Methods ◽  
Source Image ◽  
Fusion Method ◽  
Visible Image ◽  
Weighting Method ◽  
Fused Image ◽  
Fusion Methods

In many actual applications, fused image is essential to contain high-quality details for achieving a comprehensive representation of the real scene. However, existing image fusion methods suffer from loss of details because of the error accumulations of sequential tasks. This paper proposes a novel fusion method to preserve details of infrared and visible images by combining new decomposition, feature extraction, and fusion scheme. For decomposition, different from the most decomposition methods by guided filter, the guidance image contains only the strong edge of the source image but no other interference information so that rich tiny details can be decomposed into the detailed part. Then, according to the different characteristics of infrared and visible detail parts, a rough convolutional neural network (CNN) and a sophisticated CNN are designed so that various features can be fully extracted. To integrate the extracted features, we also present a multi-layer features fusion strategy through discrete cosine transform (DCT), which not only highlights significant features but also enhances details. Moreover, the base parts are fused by weighting method. Finally, the fused image is obtained by adding the fused detail and base part. Different from the general image fusion methods, our method not only retains the target region of source image but also enhances background in the fused image. In addition, compared with state-of-the-art fusion methods, our proposed fusion method has many advantages, including (i) better visual quality of fused-image subjective evaluation, and (ii) better objective assessment for those images.

scholarly journals A Heterogeneous Image Fusion Method Based on DCT and Anisotropic Diffusion for UAVs in Future 5G IoT Scenarios

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shuai Hao ◽  
Beiyi An ◽  
Hu Wen ◽  
Xu Ma ◽  
Keping Yu
Keyword(s):  
Image Fusion ◽  
Traffic Management ◽  
Anisotropic Diffusion ◽  
Weighted Averaging ◽  
Base Layer ◽  
Fusion Method ◽  
Linear Superposition ◽  
Visible Image ◽  
Fusion Methods ◽  
Visible Images

Unmanned aerial vehicles, with their inherent fine attributes, such as flexibility, mobility, and autonomy, play an increasingly important role in the Internet of Things (IoT). Airborne infrared and visible image fusion, which constitutes an important data basis for the perception layer of IoT, has been widely used in various fields such as electric power inspection, military reconnaissance, emergency rescue, and traffic management. However, traditional infrared and visible image fusion methods suffer from weak detail resolution. In order to better preserve useful information from source images and produce a more informative image for human observation or unmanned aerial vehicle vision tasks, a novel fusion method based on discrete cosine transform (DCT) and anisotropic diffusion is proposed. First, the infrared and visible images are denoised by using DCT. Second, anisotropic diffusion is applied to the denoised infrared and visible images to obtain the detail and base layers. Third, the base layers are fused by using weighted averaging, and the detail layers are fused by using the Karhunen–Loeve transform, respectively. Finally, the fused image is reconstructed through the linear superposition of the base layer and detail layer. Compared with six other typical fusion methods, the proposed approach shows better fusion performance in both objective and subjective evaluations.

scholarly journals Discrete Wavelet Transform Based Image Fusion Using Unsharp Masking

2019 ◽  
Vol 64 (2) ◽  
pp. 211-220
Author(s):  
Sumanth Kumar Panguluri ◽  
Laavanya Mohan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Fusion Rule ◽  
Discrete Wavelet ◽  
Visible Image ◽  
Unsharp Masking ◽  
Fused Image ◽  
Approximation Coefficients ◽  
Performance Results

Nowadays the result of infrared and visible image fusion has been utilized in significant applications like military, surveillance, remote sensing and medical imaging applications. Discrete wavelet transform based image fusion using unsharp masking is presented. DWT is used for decomposing input images (infrared, visible). Approximation and detailed coefficients are generated. For improving contrast unsharp masking has been applied on approximation coefficients. Then for merging approximation coefficients produced after unsharp masking average fusion rule is used. The rule that is used for merging detailed coefficients is max fusion rule. Finally, IDWT is used for generating a fused image. The result produced using the proposed fusion method is providing good contrast and also giving better performance results in reference to mean, entropy and standard deviation when compared with existing techniques.

scholarly journals Effective Multifocus Image Fusion Based on HVS and BP Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Yang ◽  
Wenjuan Zheng ◽  
Shuying Huang
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Fusion Rule ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Opening And Closing ◽  
Image Fusion Method

The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations.

A Rapid Fusion Algorithm of Infrared and the Visible Images Based on Directionlet Transform

2010 ◽  
Vol 20-23 ◽  
pp. 45-51
Author(s):  
Xiang Li ◽  
Yue Shun He ◽  
Xuan Zhan ◽  
Feng Yu Liu
Keyword(s):  
Fusion Rule ◽  
Infrared Images ◽  
Fusion Algorithm ◽  
Visible Image ◽  
Regional Energy ◽  
Fused Image ◽  
Visible Images ◽  
Simulation Results ◽  
Edge Features ◽  
Intense Color

Direction transform; image fusion; infrared images; fusion rule; anisotropic Abstract Based on analysing the feature of infrared and the visible, this paper proposed an improved algorithm using Directionlet transform.The feature is like this: firstly, separate the color visible images to get the component images, and then make anisotropic decomposition for component images and inrared images, after analysing these images, process them according to regional energy rules ,finally incorporate the intense color to get the fused image. The simulation results shows that,this algorithm can effectively fuse infrared and the visible image, moreover, not only the fused images can maintain the environment details, but also underline the edge features, which applies to fusion with strong edges, therefore,this algorithm is of robust and convenient.

scholarly journals A Novel Method for Infrared and Visible Image Fusion using Filters

2020 ◽  
Vol 8 (6) ◽  
pp. 1525-1529
Keyword(s):  
Image Fusion ◽  
Moving Average ◽  
Fusion Rule ◽  
Bilateral Filter ◽  
Source Image ◽  
Single Image ◽  
Fusion Algorithm ◽  
Visible Image ◽  
Visible Images ◽  
Novel Method

Image fusion is the process of coalescence of two or more images of the same scene taken from different sensors to produce a composite image with rich details. Due to the progression of infrared (IR) and Visible (VI) image fusion and its ever-growing demands it led to an algorithmic development of image fusion in the last several years. The two modalities have to be integrated altogether with the necessary information to form a single image. In this article, a novel image fusion algorithm has been introduced with the combination of bilateral, Robert filters as method I and moving average, bilateral filter as method II to fuse infrared and visible images. The proposed algorithm follows double - scale decomposition by using average filer and the detail information is obtained by subtracting it from the source image. Smooth and detail weights of the source images are obtained by using the two methods mentioned above. Then a weight based fusion rule is used to amalgamate the source image information into a single image. Performances of both methods are compared both qualitatively and quantitatively. Experimental results provide better results for method I compared to method II.

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