Multi-Focus Fusion Based on the Imaging Fuzzy Mechanism

2012 ◽  
Vol 239-240 ◽  
pp. 135-139
Author(s):  
Xiao Feng Yue ◽  
Tao Shen Li ◽  
Zi Xin Feng
Keyword(s):  
Image Fusion ◽  
Wavelet Decomposition ◽  
Evaluation Criteria ◽  
Original Image ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Image Block ◽  
Time Performance ◽  
Block Form ◽  
Imaging Theory

This paper put forward an image fusion algorithm which based on the analysis of the imaging theory fusion, the fusion process is first will have been registration of the original image into several pieces, we calculate the correlation of corresponding piece, as the image contrast evaluation criteria, by selecting two images in the clear image block form fusion image. This paper discusses the principle analysis from optical imaging, the cause of the fusion algorithm, to avoid the use of Gaussian Blur model to explain the image of a fuzzy controversy. The experimental results show that the proposed algorithm is good real-time performance, for registration fusion image can achieve even more than general wavelet decomposition of the fusion algorithm.

An Image Fusion Algorithm Based on Pseudo-Color

2012 ◽  
Vol 433-440 ◽  
pp. 5436-5442
Author(s):  
Lei Li
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Target Identification ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Color Processing ◽  
False Color ◽  
Fused Image ◽  
Color Fusion ◽  
Pseudo Color

The pseudo-color processing for target identification and tracking is very meaningful Experimental results show that the pseudo-color image fusion is a very effective methods. This paper presents a false color image fusion based on the new method. Fusion using wavelet transform grayscale images, find the gray fused image and the difference between the original image, respectively, as the image of l, α, β components are color fusion image, and then after the color transformation, the final false color fused image. The results showed that the color fusion image colors more vivid, more in line with human visual characteristics.

An Image Fusion Algorithm Based on Redundant Wavelet Transform

2014 ◽  
Vol 687-691 ◽  
pp. 3656-3661
Author(s):  
Min Fen Shen ◽  
Zhi Fei Su ◽  
Jin Yao Yang ◽  
Li Sha Sun
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Source Image ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Imaging Characteristics ◽  
Focus Image ◽  
Redundant Wavelet Transform

Because of the limit of the optical lens’s depth, the objects of different distance usually cannot be at the same focus in the same picture, but multi-focus image fusion can obtain fusion image with all goals clear, improving the utilization rate of the image information ,which is helpful to further computer processing. According to the imaging characteristics of multi-focus image, a multi-focus image fusion algorithm based on redundant wavelet transform is proposed in this paper. For different frequency domain of redundant wavelet decomposition, the selection principle of high-frequency coefficients and low-frequency coefficients is respectively discussed .The fusion rule is that,the selection of low frequency coefficient is based on the local area energy, and the high frequency coefficient is based on local variance combining with matching threshold. As can be seen from the simulation results, the method given in the paper is a good way to retain more useful information from the source image , getting a fusion image with all goals clear.

NSCT-Based Multi-Sensor Image Fusion Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 3212-3216
Author(s):  
Hai Feng Tan ◽  
Wen Jie Zhao ◽  
De Jun Li ◽  
Tian Wen Luo
Keyword(s):  
Image Fusion ◽  
Average Method ◽  
Low Frequency ◽  
Weighted Average ◽  
Inverse Transformation ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Focus Image ◽  
Quantitative Indicators

Against the defects that the favoritism method and average method in the multi-sensor image fusion are apt to impair the image contrast, an image fusion algorithm based on NSCT is proposed. Firstly, this algorithm applied NSCT to the rectified multi-sensor images from the same scene, then different fusion strategies were adopted to fuse the low-frequency and high-frequency directional sub-band coefficients respectively: regional energy adaptive weighted method was used for low-frequency sub-band coefficient; the directional sub-band coefficient adopted a regional-energy-matching program that combined weighted average method and selection method. Finally, the fusion image was obtained by NSCT inverse transformation. Experiments were conducted to IR and visible light image and multi-focus image respectively. And the fusion image was evaluated objectively. The experimental results show that the fusion image obtained through this algorithm has better subjective visual effects and objective quantitative indicators. It is also superior to the traditional fusion method.

AN IMAGE FUSION FRAMEWORK BASED ON HUMAN VISUAL SYSTEM IN FRAMELET DOMAIN

2012 ◽  
Vol 10 (01) ◽  
pp. 1250002 ◽  
Author(s):  
GAURAV BHATNAGAR ◽  
Q. M. JONATHAN WU
Keyword(s):  
Visual System ◽  
Image Fusion ◽  
Human Visual System ◽  
High Frequency ◽  
Evaluation Criteria ◽  
Low Frequency ◽  
Weighted Average ◽  
Objective Evaluation ◽  
Fusion Algorithm ◽  
Frequency Bands

In this paper, a novel image fusion algorithm based on framelet transform is presented. The core idea is to decompose all the images to be fused into low and high-frequency bands using framelet transform. For fusion, two different selection strategies are developed and used for low and high-frequency bands. The first strategy is adaptive weighted average based on local energy and is applied to fuse the low-frequency bands. In order to fuse high-frequency bands, a new strategy is developed based on texture while exploiting the human visual system characteristics, which can preserve more details in source images and further improve the quality of fused image. Experimental results demonstrate the efficiency and better performance than existing image fusion methods both in visual inspection and objective evaluation criteria.

Assessment of SPOT-6 optical remote sensing data against GF-1 using NNDiffuse image fusion algorithm

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740043 ◽  
Author(s):  
Jinling Zhao ◽  
Junjie Guo ◽  
Wenjie Cheng ◽  
Chao Xu ◽  
Linsheng Huang
Keyword(s):  
Image Fusion ◽  
Near Infrared ◽  
Spectral Response ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Optical Remote Sensing ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Color Quality ◽  
Fused Image

A cross-comparison method was used to assess the SPOT-6 optical satellite imagery against Chinese GF-1 imagery using three types of indicators: spectral and color quality, fusion effect and identification potential. More specifically, spectral response function (SRF) curves were used to compare the two imagery, showing that the SRF curve shape of SPOT-6 is more like a rectangle compared to GF-1 in blue, green, red and near-infrared bands. NNDiffuse image fusion algorithm was used to evaluate the capability of information conservation in comparison with wavelet transform (WT) and principal component (PC) algorithms. The results show that NNDiffuse fused image has extremely similar entropy vales than original image (1.849 versus 1.852) and better color quality. In addition, the object-oriented classification toolset (ENVI EX) was used to identify greenlands for comparing the effect of self-fusion image of SPOT-6 and inter-fusion image between SPOT-6 and GF-1 based on the NNDiffuse algorithm. The overall accuracy is 97.27% and 76.88%, respectively, showing that self-fused image of SPOT-6 has better identification capability.

An Improved Image Fusion Algorithm Based on Wavelet Decomposition

2010 ◽  
Vol 5 (10) ◽  
pp. 15-21 ◽  
Author(s):  
Li Fan ◽  
Yudong Zhang ◽  
Zhenyu Zhou ◽  
David P. Semanek ◽  
Shuihua Wang ◽  
...  
Keyword(s):  
Image Fusion ◽  
Wavelet Decomposition ◽  
Fusion Algorithm

An Improved Compressive Sensing Image Fusion Algorithm Based on NSCT Transform

2013 ◽  
Vol 433-435 ◽  
pp. 306-309 ◽  
Author(s):  
Yan Hai Wu ◽  
Di Yan ◽  
Meng Xin Ma ◽  
Nan Wu
Keyword(s):  
Image Fusion ◽  
Compressive Sensing ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Absolute Value ◽  
Transmission Data ◽  
Improved Algorithm

A modified compressive sensing image fusion algorithm is proposed in this paper that is based on the NSCT transform. The algorithm is improved by introducing the theory of compressive sensing into image fusion that uses the NSCT transform to make a specific image be sparse on which only the high frequency coefficient is specifically measured; The improved algorithm then process the image fusion by retrieving the maximal value of the gradient of the neighborhood average from the measured high frequency coefficient, and accordingly, maximizing the absolute value of the neighborhood variance to the low-frequency counterpart. Afterwards, the improved algorithm can reconfigure the fusion image by using the MSP reconfiguration algorithm with final deliverable of the fusion image by committing to the NSCT reverse transform. Simulation results show that the improved algorithm is superior to other hand-on algorithms both in visual effect and in objective evaluation. In the case that the storage and transmission data are limited, the algorithm comes forth better effect of image fusion that is verified to be possesses of high value in practice.

scholarly journals Multifocus Image Fusion Using Biogeography-Based Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ping Zhang ◽  
Chun Fei ◽  
Zhenming Peng ◽  
Jianping Li ◽  
Hongyi Fan
Keyword(s):  
Image Fusion ◽  
Fitness Function ◽  
Block Size ◽  
Source Image ◽  
Morphological Filter ◽  
Fusion Algorithm ◽  
Image Block ◽  
Chaotic Search ◽  
Mutation Operation ◽  
Fused Image

For multifocus image fusion in spatial domain, sharper blocks from different source images are selected to fuse a new image. Block size significantly affects the fusion results and a fixed block size is not applicable in various multifocus images. In this paper, a novel multifocus image fusion algorithm using biogeography-based optimization is proposed to obtain the optimal block size. The sharper blocks of each source image are first selected by sum modified Laplacian and morphological filter to contain an initial fused image. Then, the proposed algorithm uses the migration and mutation operation of biogeography-based optimization to search the optimal block size according to the fitness function in respect of spatial frequency. The chaotic search is adopted during iteration to improve optimization precision. The final fused image is constructed based on the optimal block size. Experimental results demonstrate that the proposed algorithm has good quantitative and visual evaluations.

scholarly journals MEDICAL IMAGES FUSION ALGORITHM BASED ON PROBABILISTIC GAMMA-NORMAL MODEL WITH STRUCTURE-TRANSFERRING PROPERTIES

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 79-83
Author(s):  
I. A. Gracheva ◽  
A. V. Kopylov
Keyword(s):  
Image Fusion ◽  
Main Idea ◽  
Computation Time ◽  
Vital Role ◽  
Normal Model ◽  
Mr Images ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Pixel Intensity ◽  
Weighted Fusion

Abstract. In medical image processing, image fusion is the process of combining complementary information from different (multimodality) images to obtain a fused image, which plays a vital role in further analysis and treatment planning. The main idea of this paper is to improve the image content by fusing computer tomography (CT) and magnetic resonance (MR) images. We propose here the new algorithm based on the probabilistic gamma-normal model with structure-transferring properties. Firstly, we select the areas with the highest pixel intensity on original CT and MR images. In parallel with this, the structures of original images are distinguished using the probabilistic gamma-normal model. The weighted-fusion image can be obtained based on detected objects and structure. Finally, we smooth the weighted-fusion image using the structure-transferring filter and combine the smoothed image with the weighted-fusion image for obtaining the resulting image. The key point here is that we do not need to re-allocate the structure, which leads to the reduction of computation time. The proposed method gives the best result in terms of the spatial frequency metric and lower computation time than other image fusion methods.

Overview of Pixel Level Image Fusion Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 590-593 ◽  
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Jie Li
Keyword(s):  
Image Fusion ◽  
Frequency Domain ◽  
Time Domain ◽  
Evaluation Criteria ◽  
Weighted Average ◽  
Principal Component ◽  
Matlab Simulation ◽  
Fusion Algorithm ◽  
Evaluation Standard ◽  
Fused Image

Pixel level image fusion algorithm is one of the basic algorithms in image fusion, which is mainly divided into time domain and frequency domain algorithm. The weighted average algorithm and PCA (principal component analysis) are popular algorithms in time domain. Pyramid algorithm and wavelet algorithm are usually used to fuse two or multiple images in frequency domain. In this paper, pixel level image fusion algorithm was summarized, including of operation, characteristics and application etc. MATLAB simulation shows that effect of frequency domain algorithm is better than time domain algorithm. Evaluation criteria mainly refer to entropy, cross entropy, the mean and standard deviation etc. Evaluation standard is the reference of fusion effects, different evaluation criteria could be selected according to different fused image and different fusion purpose.

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