Infrared and visible image fusion using dual-tree complex wavelet transform and convolutional sparse representation

2020 ◽  
Vol 39 (3) ◽  
pp. 4617-4629
Author(s):  
Chengrui Gao ◽  
Feiqiang Liu ◽  
Hua Yan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Low Frequency ◽  
Infrared Images ◽  
Complex Wavelet Transform ◽  
Visible Image ◽  
Visible Images ◽  
Complex Wavelet

Infrared and visible image fusion refers to the technology that merges the visual details of visible images and thermal feature information of infrared images; it has been extensively adopted in numerous image processing fields. In this study, a dual-tree complex wavelet transform (DTCWT) and convolutional sparse representation (CSR)-based image fusion method was proposed. In the proposed method, the infrared images and visible images were first decomposed by dual-tree complex wavelet transform to characterize their high-frequency bands and low-frequency band. Subsequently, the high-frequency bands were enhanced by guided filtering (GF), while the low-frequency band was merged through convolutional sparse representation and choose-max strategy. Lastly, the fused images were reconstructed by inverse DTCWT. In the experiment, the objective and subjective comparisons with other typical methods proved the advantage of the proposed method. To be specific, the results achieved using the proposed method were more consistent with the human vision system and contained more texture detail information.

Fusion of Visible and Infrared Images Based on Non-Sampling Contourlet and Wavelet Transform

2014 ◽  
Vol 599-601 ◽  
pp. 1523-1526
Author(s):  
Yan Hai Wu ◽  
Hao Zhang ◽  
Fang Ni Zhang ◽  
Yue Hua Han
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Infrared Image ◽  
Low Frequency ◽  
Infrared Images ◽  
Fusion Image ◽  
Visible Image ◽  
Fusion Rules ◽  
Visible Images

This paper gives a method for fusion of visible and infrared image, which combined non-sampling contourlet and wavelet transform. This method firstly makes contrast enhancements to infrared image. Next, does NSCT decomposition to visible image and enhanced-infrared image, then decomposes the low frequency from above decomposition using wavelet. Thirdly, for high-frequency subband of NSCT decomposition and high or low-frequency subband of wavelet, it uses different fusion rules. Finally, it gets fusion image through refactoring of wavelet and NSCT. Experiments show that the method not only retains texture details belong to visible images, but also highlights targets in infrared images. It has a better fusion effect.

DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

2021 ◽  
Vol 12 (4) ◽  
pp. 78-97
Author(s):  
Hassiba Talbi ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Weighted Average ◽  
Optimization Method ◽  
Absolute Maximum ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.

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

2019 ◽  
Vol 17 (06) ◽  
pp. 1950045 ◽  
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.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy

Oncology ◽  
2017 ◽  
pp. 519-541
Author(s):  
Satishkumar S. Chavan ◽  
Sanjay N. Talbar
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Frequency Domain ◽  
Medical Image ◽  
Treatment Plan ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion ◽  
Daubechies Complex Wavelet Transform ◽  
Fused Image ◽  
Complex Wavelet

The process of enriching the important details from various modality medical images by combining them into single image is called multimodality medical image fusion. It aids physicians in terms of better visualization, more accurate diagnosis and appropriate treatment plan for the cancer patient. The combined fused image is the result of merging of anatomical and physiological variations. It allows accurate localization of cancer tissues and more helpful for estimation of target volume for radiation. The details from both modalities (CT and MRI) are extracted in frequency domain by applying various transforms and combined them using variety of fusion rules to achieve the best quality of images. The performance and effectiveness of each transform on fusion results is evaluated subjectively as well as objectively. The fused images by algorithms in which feature extraction is achieved by M-Band Wavelet Transform and Daubechies Complex Wavelet Transform are superior over other frequency domain algorithms as per subjective and objective analysis.

scholarly journals Airborne Infrared and Visible Image Fusion for Target Perception Based on Target Region Segmentation and Discrete Wavelet Transform

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Yifeng Niu ◽  
Shengtao Xu ◽  
Lizhen Wu ◽  
Weidong Hu
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Infrared Images ◽  
Target Information ◽  
Visible Image ◽  
Region Segmentation ◽  
Target Region ◽  
Fused Image

Infrared and visible image fusion is an important precondition of realizing target perception for unmanned aerial vehicles (UAVs), then UAV can perform various given missions. Information of texture and color in visible images are abundant, while target information in infrared images is more outstanding. The conventional fusion methods are mostly based on region segmentation; as a result, the fused image for target recognition could not be actually acquired. In this paper, a novel fusion method of airborne infrared and visible image based on target region segmentation and discrete wavelet transform (DWT) is proposed, which can gain more target information and preserve more background information. The fusion experiments are done on condition that the target is unmoving and observable both in visible and infrared images, targets are moving and observable both in visible and infrared images, and the target is observable only in an infrared image. Experimental results show that the proposed method can generate better fused image for airborne target perception.

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