scholarly journals A Multi-exposure Image Fusion Method with Detail Preservation

2018 ◽  
Vol 173 ◽  
pp. 03009
Author(s):  
Chen Shouhong ◽  
Zhao Shuang ◽  
Ma Jun ◽  
Liu Xinyu ◽  
Hou Xingna
Keyword(s):  
Image Fusion ◽  
Spot Welding ◽  
Local Contrast ◽  
Color Information ◽  
Fusion Algorithm ◽  
Quality Requirements ◽  
Welding Image ◽  
Contrast Exposure ◽  
The Rich ◽  
Image Fusion Method

In view of the problems of uneven exposure in the image acquisition and the serious loss of details in the traditional multi-exposure image fusion algorithm, a method of image fusion with details preservation is proposed. A weighted approach to multi-exposure image fusion is used, taking into account the features such as local contrast, exposure brightness, and color information to better preserve detail. For the purpose of eliminating the noise and interference, using the recursive filter to filter. Compared with other algorithms, the proposed algorithm can retain the rich detail information to meet the quality requirements of spot welding image fusion and has certain application value.

Local Contrast Preserved Image Fusion Method Using Two Differently Exposed Images

2015 ◽  
Vol 13 (11) ◽  
pp. 25
Author(s):  
Geun-Young Lee ◽  
Hyuk-Ju Kwon ◽  
Sung-Hak Lee ◽  
Kyu-Ik Sohng
Keyword(s):  
Image Fusion ◽  
Local Contrast ◽  
Fusion Method ◽  
Image Fusion Method

Contourlet Transform in the Application of the Image Fusion

2012 ◽  
Vol 239-240 ◽  
pp. 1432-1436
Author(s):  
Zhuan Zheng Zhao
Keyword(s):  
Image Fusion ◽  
Image Resolution ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Fusion Algorithm ◽  
Fused Image ◽  
New Interpretation ◽  
Image Fusion Method

Image Fusion is integrating two or more sensors at the same time or at different times of image or videos equenece to generate a new interpretation of this scene. Its main purpose is increasing reliability or image resolution by redueing uncertainty through redundancy of different images.In this paper, a image fusion method based on contourlet transform is presented. The algorithm can fuse corresponding information in different resolutions and directions, which makes the fused image clearer and more abundant in details. Meanwhile, because of the fuzzy logic’s capacity of resolving uncertain problems, it overcomes the drawbacks of traditional fusion algorithm based on contourlet transform, and integrates as much information as possible into the fused image.

Research and Development of Non Multi-Scale to Pixel-Level Image Fusion

2013 ◽  
Vol 448-453 ◽  
pp. 3621-3624 ◽  
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Xiao Li Wang
Keyword(s):  
Image Fusion ◽  
Basic Principle ◽  
Weighted Average ◽  
Source Image ◽  
Fusion Method ◽  
Fusion Algorithm ◽  
Multi Scale ◽  
Imaging Characteristics ◽  
Fusion Methods ◽  
Image Fusion Method

Image fusion method based on the non multi-scale take the original image as object of study, using various fusion rule of image fusion to fuse images, but not decomposition or transform to original images. So, it can also be called simple multi sensor image fusion methods. Its advantages are low computational complexity and simple principle. Image fusion method based on the non multi-scale is currently the most widely used image fusion methods. The basic principle of fuse method is directly to select large gray, small gray and weighted average among pixel on the source image, to fuse into a new image. Simple pixel level image fusion method mainly includes the pixel gray value being average or weighted average, pixel gray value being selected large and pixel gray value being selected small, etc. Basic principle of fusion process was introduced in detail in this paper, and pixel level fusion algorithm at present was summed up. Simulation results on fusion are presented to illustrate the proposed fusion scheme. In practice, fusion algorithm was selected according to imaging characteristics being retained.

scholarly journals A Noisy SAR Image Fusion Method Based on NLM and GAN

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 410
Author(s):  
Jing Fang ◽  
Xiaole Ma ◽  
Jingjing Wang ◽  
Kai Qin ◽  
Shaohai Hu ◽  
...  
Keyword(s):  
Image Fusion ◽  
Generative Adversarial Networks ◽  
Fusion Method ◽  
Sar Image ◽  
Color Information ◽  
Image Block ◽  
Sar Images ◽  
Adversarial Networks ◽  
Fused Image ◽  
Image Fusion Method

The unavoidable noise often present in synthetic aperture radar (SAR) images, such as speckle noise, negatively impacts the subsequent processing of SAR images. Further, it is not easy to find an appropriate application for SAR images, given that the human visual system is sensitive to color and SAR images are gray. As a result, a noisy SAR image fusion method based on nonlocal matching and generative adversarial networks is presented in this paper. A nonlocal matching method is applied to processing source images into similar block groups in the pre-processing step. Then, adversarial networks are employed to generate a final noise-free fused SAR image block, where the generator aims to generate a noise-free SAR image block with color information, and the discriminator tries to increase the spatial resolution of the generated image block. This step ensures that the fused image block contains high resolution and color information at the same time. Finally, a fused image can be obtained by aggregating all the image blocks. By extensive comparative experiments on the SEN1–2 datasets and source images, it can be found that the proposed method not only has better fusion results but is also robust to image noise, indicating the superiority of the proposed noisy SAR image fusion method over the state-of-the-art methods.

An Image Fusion Method Based on Shearlet and Multi-Decision

2014 ◽  
Vol 530-531 ◽  
pp. 390-393
Author(s):  
Yong Wang
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Image Decomposition ◽  
Fusion Algorithm ◽  
Shearlet Transform ◽  
Fused Image ◽  
Focus Image ◽  
Image Detail ◽  
Image Fusion Method ◽  
Traditional Image

Image processing is the basis of computer vision. Aiming at some problems existed in the traditional image fusion algorithm, a novel algorithm based on shearlet and multi-decision is proposed. At first we discussed multi-focus image fusion and then we use Shearlet transform and multi-decision for image decomposition high-frequency coefficients. Finally, the fused image is obtained through inverse Shearlet transform. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach retains image detail and more clarity.

An Efficient Cancer Classification Model for CT/MRI/PET Fused Images

2020 ◽  
Vol 16 ◽  
Author(s):  
S. Srimathi ◽  
G. Yamuna ◽  
R. Nanmaran
Keyword(s):  
Image Fusion ◽  
Treatment Plan ◽  
Curvelet Transform ◽  
Cancer Classification ◽  
Pet Scan ◽  
Classification Model ◽  
Fusion Method ◽  
Fusion Algorithm ◽  
Data Set ◽  
Image Fusion Method

Objective: Image fusion-based cancer classification models used to diagnose cancer and assessment of medical problems in earlier stages that help doctors or health care professionals to plan the treatment plan accordingly. Methods : In this work, a novel Curvelet transform-based image fusion method is developed. CT and PET scan images of benign type tumors fused together using the proposed fusion algorithm and the same way MRI and PET scan images of malignant type tumors fused together to achieve the combined benefits of individual imaging techniques. Then the Marker controlled watershed Algorithm applied on fused image to segment cancer affected area. The various color features, shape features and texture-based features extracted from the segmented image. Then a data set formed with various features, which have given as input to different classifiers namely neural network classifier, Random forest classifier, K-NN classifier to determine the nature of cancer. The results of the classifier will be Normal, Benign or Malignant category of cancer. Results: The performance of the proposed fusion Algorithm compared with existing fusion techniques based on the parameters PSNR, SSIM, Entropy, Mean and Standard Deviation. Curvelet transform based fusion method performs better than already existing methods in terms of five parameters. The performances of classifiers are evaluated using three parameters Accuracy, Sensitivity, and Specificity. K-NN Classifier performs better when compared to the other two classifiers and it provides overall accuracy of 94%, Sensitivity of 88% and Specificity of 84%. Conclusion: The proposed Curvelet transform based image fusion method combined with the K-NN classifier provides better results when compared to other two classifiers when two input images used individually.

IMAGE FUSION METHOD BASED ON SHORT SUPPORT SYMMETRIC NON-SEPARABLE WAVELET

2004 ◽  
Vol 02 (01) ◽  
pp. 87-98 ◽  
Author(s):  
LIU BIN ◽  
JIAXIONG PENG
Keyword(s):  
Standard Deviation ◽  
Image Fusion ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Activity Measurement ◽  
Fusion Method ◽  
Fusion Algorithm ◽  
Dilation Matrix ◽  
Image Fusion Method

In this paper, image fusion method based on a new class of wavelet — non-separable wavelet with compactly supported, linear phase, orthogonal and dilation matrix [Formula: see text] is presented. We first construct a non-separable wavelet filter bank. Using these filters, the images involved are decomposed into wavelet pyramids. Then the following fusion algorithm was proposed: for low-frequency part, the average value is selected for new pixel value, For the three high-frequency parts of each level, the standard deviation of each image patch over 3×3 window in the high-frequency sub-images is computed as activity measurement. If the standard deviation of the area 3×3 window is bigger than the standard deviation of the corresponding 3×3 window in the other high-frequency sub-image. The center pixel values of the area window that the weighted area energy is bigger are selected. Otherwise the weighted value of the pixel is computed. Then a new fused image is reconstructed. The performance of the method is evaluated using the entropy, cross-entropy, fusion symmetry, root mean square error and peak-to-peak signal-to-noise ratio. The experiment results show that the non-separable wavelet fusion method proposed in this paper is very close to the performance of the Haar separable wavelet fusion method.

scholarly journals An Improved Multi-Exposure Image Fusion Method for Intelligent Transportation System

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 383
Author(s):  
Mingyu Gao ◽  
Junfan Wang ◽  
Yi Chen ◽  
Chenjie Du ◽  
Chao Chen ◽  
...  
Keyword(s):  
Image Fusion ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Source Image ◽  
Fusion Method ◽  
Fusion Algorithm ◽  
Traffic Signs ◽  
Image Fusion Method

In this paper, an improved multi-exposure image fusion method for intelligent transportation systems (ITS) is proposed. Further, a new multi-exposure image dataset for traffic signs, TrafficSign, is presented to verify the method. In the intelligent transportation system, as a type of important road information, traffic signs are fused by this method to obtain a fused image with moderate brightness and intact information. By estimating the degree of retention of different features in the source image, the fusion results have adaptive characteristics similar to that of the source image. Considering the weather factor and environmental noise, the source image is preprocessed by bilateral filtering and dehazing algorithm. Further, this paper uses adaptive optimization to improve the quality of the output image of the fusion model. The qualitative and quantitative experiments on the new dataset show that the multi-exposure image fusion algorithm proposed in this paper is effective and practical in the ITS.

Image Fusion Algorithm Based on Contourlet Transform

2014 ◽  
Vol 1044-1045 ◽  
pp. 1173-1177
Author(s):  
Yu Kun Zhang ◽  
Shu He ◽  
Yong Jun Cheng
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Contourlet Transform ◽  
Analysis Tool ◽  
Fusion Algorithm ◽  
Multi Scale ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Application Requirements ◽  
Image Fusion Method

Contourlet transform is a new multi-scale, multi-resolution analysis tool. This paper studied on the theory of Contourlet transform.,According to the practical application requirements of characteristics of data, much details in complex images it propose a novel image fusion method of remote sensing images based on contourlet Coefficients correlativity of directional region. Besides improving fused images spatial resolution, our method can better preserve original multi-spectral image’s color information.Extensive experimental results show that the proposed method is superior to conventional methods in terms of entropy, joint entropy, and average gradient. It can enhance the spatial resolution of target images. Meanwhile, it well preserves the color information of multi-spectral images.

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