A Novel Method for Infrared and Visible Image Fusion using Filters

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

10.21203/rs.2.24788/v2 ◽

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.

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

Electronics ◽

10.3390/electronics11010033 ◽

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.

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

10.21203/rs.2.24788/v1 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.45 ◽

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.

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

Sensors ◽

10.3390/s22010040 ◽

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.

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An Image Fusion Algorithm Based on NSCT

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1589 ◽

2013 ◽

Vol 427-429 ◽

pp. 1589-1592

Author(s):

Zhong Jie Xiao

Keyword(s):

Image Fusion ◽

High Frequency ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Method ◽

Fusion Algorithm ◽

Visible Image ◽

Weighted Fusion ◽

Scene Information ◽

Feature Information

The study proposed an improved NSCT fusion method based on the infrared and visible light images characteristics and fusion requirement. This paper improved the high-frequency coefficient and low-frequency coefficient fusion rules. The low-frequency sub-band images adopted the pixel feature energy weighted fusion rule. The high-frequency sub-band images adopted the neighborhood variance feature information fusion rule. The fusion experiment results show that this algorithm has good robustness. It could effectively extract edges and texture information. The fused images have abundance scene information and clear target. So this algorithm is an effective infrared and visible image fusion method.

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Infrared and visible image fusion method based on rolling guidance filter and NSST

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319500450 ◽

2019 ◽

Vol 17 (06) ◽

pp. 1950045 ◽

Cited By ~ 2

Author(s):

Cheng Zhao ◽

Yongdong Huang

Keyword(s):

Image Fusion ◽

High Frequency ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Method ◽

Visible Image ◽

Guided Filtering ◽

Gradient Domain ◽

Visible Images ◽

Image Fusion Method

The rolling guidance filtering (RGF) has a good characteristic which can smooth texture and preserve the edges, and non-subsampled shearlet transform (NSST) has the features of translation invariance and direction selection based on which a new infrared and visible image fusion method is proposed. Firstly, the rolling guidance filter is used to decompose infrared and visible images into the base and detail layers. Then, the NSST is utilized on the base layer to get the high-frequency coefficients and low-frequency coefficients. The fusion of low-frequency coefficients uses visual saliency map as a fusion rule, and the coefficients of the high-frequency subbands use gradient domain guided filtering (GDGF) and improved Laplacian sum to fuse coefficients. Finally, the fusion of the detail layers combines phase congruency and gradient domain guided filtering as the fusion rule. As a result, the proposed method can not only extract the infrared targets, but also fully preserves the background information of the visible images. Experimental results indicate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.

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Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques

10.32920/ryerson.14655834 ◽

2021 ◽

Author(s):

Anuyogam Venkataraman

Keyword(s):

Computed Tomography ◽

Image Fusion ◽

Region Of Interest ◽

Source Image ◽

High Dose ◽

Fusion Algorithm ◽

Quantitative Metrics ◽

Fused Image ◽

Fusion Methods ◽

Computed Tomography Image

With the increasing utilization of X-ray Computed Tomography (CT) in medical diagnosis, obtaining higher quality image with lower exposure to radiation is a highly challenging task in image processing. Sparse representation based image fusion is one of the sought after fusion techniques among the current researchers. A novel image fusion algorithm based on focused vector detection is proposed in this thesis. Firstly, the initial fused vector is acquired by combining common and innovative sparse components of multi-dosage ensemble using Joint Sparse PCA fusion method utilizing an overcomplete dictionary trained using high dose images of the same region of interest from different patients. And then, the strongly focused vector is obtained by determining the pixels of low dose and medium dose vectors which have high similarity with the pixels of the initial fused vector using certain quantitative metrics. Final fused image is obtained by denoising and simultaneously integrating the strongly focused vector, initial fused vector and source image vectors in joint sparse domain thereby preserving the edges and other critical information needed for diagnosis. This thesis demonstrates the effectiveness of the proposed algorithms when experimented on different images and the qualitative and quantitative results are compared with some of the widely used image fusion methods.

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