scholarly journals A New Algorithm for Image Fusion to reduce computational Time

2016 ◽  
Vol 15 (4) ◽  
pp. 6698-6701
Author(s):  
Navjot Kaur ◽  
Navneet Kaur
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Threshold Value ◽  
Computational Time ◽  
Mean Square ◽  
Fusion Algorithm ◽  
Whole Process ◽  
Fused Image

Image fusion is a process to combine two or more images so that fused image becomes more informative than input images. Fusion process provides the spectral and spatial information of image. But main problem occurs of computational time when high resolution images are fused. So this paper describe a new algorithm that is based on wavelet transform in which transform is applied after forming the image into different blocks. This algorithm divides the complete image into different blocks andthen comparing the images by finding the mean square error.By using the threshold value wavelet transform is applied to require block. The transformed blocks are fused by using different fusion algorithms like averaging method, maximum or minimum pixel replacement fusion algorithm. By applying inverse of wavelet transform fused image is constructed which is more informative than the input images. The quality of fused image is find out by comparing the fused image by the original image by finding mean square error and peak signal to noise ratio. The whole process of fusion is applied on the complete image and also by using blocking method then by finding the time parameters it can be conclude that the proposed algorithm reduces the computational time by 10 times to the existence method.

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform

2017 ◽  
pp. 143-157
Author(s):  
Girraj Prasad Rathor ◽  
Sanjeev Kumar Gupta
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Signal To Noise Ratio ◽  
Fusion Rule ◽  
Weighted Averaging ◽  
Adaptive Fuzzy ◽  
Fused Image ◽  
Adaptive Fuzzy Logic ◽  
Multimodality Image

Image fusion based on different wavelet transform is the most commonly used image fusion method, which fuses the source pictures data in wavelet space as per some fusion rules. But, because of the uncertainties of the source images contributions to the fused image, to design a good fusion rule to incorporate however much data as could reasonably be expected into the fused picture turns into the most vital issue. On the other hand, adaptive fuzzy logic is the ideal approach to determine uncertain issues, yet it has not been utilized as a part of the outline of fusion rule. A new fusion technique based on wavelet transform and adaptive fuzzy logic is introduced in this chapter. After doing wavelet transform to source images, it computes the weight of each source images coefficients through adaptive fuzzy logic and then fuses the coefficients through weighted averaging with the processed weights to acquire a combined picture: Mutual Information, Peak Signal to Noise Ratio, and Mean Square Error as criterion.

Image Fusion Algorithm Based on Wavelet Transform and Laplacian Pyramid

2013 ◽  
Vol 860-863 ◽  
pp. 2846-2849
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Xiao Li Wang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Relevant Information ◽  
Experimental Results ◽  
Improve Quality ◽  
Laplacian Pyramid ◽  
Single Image ◽  
Fusion Algorithm ◽  
Fused Image

Image fusion is process which combine relevant information from two or more images into a single image. The aim of fusion is to extract relevant information for research. According to different application and characteristic of algorithm, image fusion algorithm could be used to improve quality of image. This paper complete compare analyze of image fusion algorithm based on wavelet transform and Laplacian pyramid. In this paper, principle, operation, steps and characteristic of fusion algorithm are summarized, advantage and disadvantage of different algorithm are compared. The fusion effects of different fusion algorithm are given by MATLAB. Experimental results shows that quality of fused image would be improve obviously.

scholarly journals Light field camera all-in-focus image acquisition based on angular information

2021 ◽  
Vol 51 (2) ◽  
Author(s):  
Yingchun Wu, , , , ◽  
Xing Cheng ◽  
Jie Liang ◽  
Anhong Wang ◽  
Xianling Zhao
Keyword(s):  
Image Fusion ◽  
Field Data ◽  
Light Field ◽  
Spatial Information ◽  
Human Perception ◽  
Energy Difference ◽  
Image Feature ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Focus Image

Traditional light field all-in-focus image fusion algorithms are based on the digital refocusing technique. Multi-focused images converted from one single light field image are used to calculate the all-in-focus image and the light field spatial information is used to accomplish the sharpness evaluation. Analyzing the 4D light field from another perspective, an all-in-focus image fusion algorithm based on angular information is presented in this paper. In the proposed method, the 4D light field data are fused directly and a macro-pixel energy difference function based on angular information is established to accomplish the sharpness evaluation. Then the fused 4D data is guided by the dimension increased central sub-aperture image to obtain the refined 4D data. Finally, the all-in-focus image is calculated by integrating the refined 4D light field data. Experimental results show that the fused images calculated by the proposed method have higher visual quality. Quantitative evaluation results also demonstrate the performance of the proposed algorithm. With the light field angular information, the image feature-based index and human perception inspired index of the fused image are improved.

Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform

Biometrics ◽  
2017 ◽  
pp. 1754-1768
Author(s):  
Girraj Prasad Rathor ◽  
Sanjeev Kumar Gupta
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Signal To Noise Ratio ◽  
Fusion Rule ◽  
Weighted Averaging ◽  
Adaptive Fuzzy ◽  
Fused Image ◽  
Adaptive Fuzzy Logic ◽  
Multimodality Image

Image fusion based on different wavelet transform is the most commonly used image fusion method, which fuses the source pictures data in wavelet space as per some fusion rules. But, because of the uncertainties of the source images contributions to the fused image, to design a good fusion rule to incorporate however much data as could reasonably be expected into the fused picture turns into the most vital issue. On the other hand, adaptive fuzzy logic is the ideal approach to determine uncertain issues, yet it has not been utilized as a part of the outline of fusion rule. A new fusion technique based on wavelet transform and adaptive fuzzy logic is introduced in this chapter. After doing wavelet transform to source images, it computes the weight of each source images coefficients through adaptive fuzzy logic and then fuses the coefficients through weighted averaging with the processed weights to acquire a combined picture: Mutual Information, Peak Signal to Noise Ratio, and Mean Square Error as criterion.

scholarly journals Multi-Focus Image Fusion using Non-Local Mean Filtering and Stationary Wavelet Transform

2019 ◽  
Vol 9 (1) ◽  
pp. 344-350 ◽  
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Performance Metrics ◽  
Depth Of Field ◽  
Stationary Wavelet Transform ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Focus Image ◽  
Local Mean ◽  
Non Local

Today’s research era, image fusion is a actual step by step procedure to develop the visualization of any image. It integrates the essential features of more than a couple of images into a individual fused image without taking any artifacts. Multifocus image fusion has a vital key factor in fusion process where it aims to increase the depth of field using extracting focused part from different multiple focused images. In this paper multi-focus image fusion algorithm is proposed where non local mean technique is used in stationary wavelet transform (SWT) to get the sharp and smooth image. Non-local mean function analyses the pixels belonging to the blurring part and improves the image quality. The proposed work is compared with some existing methods. The results are analyzed visually as well as using performance metrics.

scholarly journals Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion

2021 ◽  
pp. 3228-3236
Author(s):  
Nada Jasim Habeeb
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Wiener Filter ◽  
Fusion Rule ◽  
Depth Of Field ◽  
Complex Wavelet Transform ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Fusion Operation ◽  
Complex Wavelet

Combining multi-model images of the same scene that have different focus distances can produce clearer and sharper images with a larger depth of field. Most available image fusion algorithms are superior in results. However, they did not take into account the focus of the image. In this paper a fusion method is proposed to increase the focus of the fused image and to achieve highest quality image using the suggested focusing filter and Dual Tree-Complex Wavelet Transform. The focusing filter consist of a combination of two filters, which are Wiener filter and a sharpening filter. This filter is used before the fusion operation using Dual Tree-Complex Wavelet Transform. The common fusion rules, which are the average-fusion rule and maximum-fusion rule, were used to obtain the fused image. In the experiment, using the focus operators, the performance of the proposed fusion algorithm was compared with the existing algorithms. The results showed that the proposed method is better than these fusion methods in terms of the focus and quality. 

scholarly journals Fusion Algorithm of Multi-focus Images with Weighted Ratios and Weighted Gradient Based on Wavelet Transform

2019 ◽  
Vol 28 (4) ◽  
pp. 505-516
Author(s):  
Wei-bin Chen ◽  
Mingxiao Hu ◽  
Lai Zhou ◽  
Hongbin Gu ◽  
Xin Zhang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Low Frequency ◽  
Fusion Rule ◽  
Fusion Algorithm ◽  
Clear Image ◽  
Fused Image ◽  
Gradient Based ◽  
Focus Image ◽  
Imaging Conditions

Abstract Multi-focus image fusion means fusing a completely clear image with a set of images of the same scene and under the same imaging conditions with different focus points. In order to get a clear image that contains all relevant objects in an area, the multi-focus image fusion algorithm is proposed based on wavelet transform. Firstly, the multi-focus images were decomposed by wavelet transform. Secondly, the wavelet coefficients of the approximant and detail sub-images are fused respectively based on the fusion rule. Finally, the fused image was obtained by using the inverse wavelet transform. Among them, for the low-frequency and high-frequency coefficients, we present a fusion rule based on the weighted ratios and the weighted gradient with the improved edge detection operator. The experimental results illustrate that the proposed algorithm is effective for retaining the detailed images.

scholarly journals Infrared Target Detection and Location for Visual Surveillance Using Fusion Scheme of Visible and Infrared Images

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Zi-Jun Feng ◽  
Xiao-Ling Zhang ◽  
Li-Yong Yuan ◽  
Jia-Nan Wang
Keyword(s):  
Image Fusion ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Infrared Image ◽  
Equation Model ◽  
Visual Surveillance ◽  
Infrared Images ◽  
Visible Image ◽  
Fused Image ◽  
Fusion Scheme

The main goal of image fusion is to combine substantial information from different images of the same scene into a single image that is suitable for human and machine perception or for further image-processing tasks. In this study, a simple and efficient image fusion approach based on the application of the histogram of infrared images is proposed. A fusion scheme to select adaptively weighted coefficients for preserving salient infrared targets from the infrared image and for obtaining most spatial detailed information from the visible image is presented. Moving and static infrared targets in the fused image are labeled with different colors. This technique enhances perception of the image for the human visual system. In view of the modalities of infrared images, low resolution, and low signal-to-noise ratio, an anisotropic diffusion equation model is adopted to remove noise and to effectively preserve edge information before the fusion stage. By using the proposed method, relevant spatial information is preserved and infrared targets are clearly identified in the resulting fused images.

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.

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