Infrared and visible image fusion with spectral graph wavelet transform

2015 ◽  
Vol 32 (9) ◽  
pp. 1643 ◽  
Author(s):  
Xiang Yan ◽  
Hanlin Qin ◽  
Jia Li ◽  
Huixin Zhou ◽  
Jing-guo Zong
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Visible Image ◽  
Spectral Graph

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.

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.

Multi-focus image fusion based on improved spectral graph wavelet transform

2015 ◽  
Author(s):  
Xiang Yan ◽  
Hanlin Qin ◽  
Zhimin Chen ◽  
Huixin Zhou ◽  
Jia Li ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Spectral Graph ◽  
Focus Image

Infrared Image and Visible Image Fusion Based on Wavelet Transform

2013 ◽  
Vol 756-759 ◽  
pp. 2850-2856 ◽  
Author(s):  
Ze Hua Zhou ◽  
Min Tan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Infrared Image ◽  
Low Frequency ◽  
Visible Image ◽  
Detection And Identification ◽  
Single Sensor ◽  
Physical Phenomena ◽  
Image Fusion Method

The same scene, the infrared image and visible image fusion can concurrently take advantage of the original image information can overcome the limitations and differences of a single sensor image in terms of geometric, spectral and spatial resolution, to improve the quality of the image , which help to locate, identify and explain the physical phenomena and events. Put forward a kind of image fusion method based on wavelet transform. And for the wavelet decomposition of the frequency domain, respectively, discussed the principles of select high-frequency coefficients and low frequency coefficients, highlight the contours of parts and the weakening of the details section, fusion, image fusion has the characteristics of two or multiple images, more people or the visual characteristics of the machine, the image for further analysis and understanding, detection and identification or tracking of the target image.

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.

Absolute Transformation and Clahe Based High Performance Lucid Proposal for Image Processing

2020 ◽  
Vol 17 (8) ◽  
pp. 3660-3670
Author(s):  
N. Archana ◽  
S. Mahalakshmi ◽  
R. Dhanagopal ◽  
R. Menaka
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Performance ◽  
Infrared Image ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Transformation Technique ◽  
Visible Image ◽  
Pixel Intensity

Image fusion is a one of the enhancement technique which is used to take the decision the images by the various types of sensors. Image fusion is nothing but the combination of two images which is helps to improve the quality of the image. In this paper, visible image and Infrared image are combined to acquire the informative image. Before and after image fusion, a new transformation technique is introduced to improve the quality of the image. To prove the quality of the image after applying new transformation technique, the fusion is done by four different techniques is used like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Non-Subsampled Contourlet Transform (NSCT) and Dual Tree Complex Wavelet Transform (DT-CWT). The comparison of following parameter values such as Entropy, Standard deviation, Mean gradient, Average pixel intensity and spatial frequency shows that proposed method is better to improve the image quality.

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