scholarly journals Image Fusion Algorithm at Pixel Level Based on Edge Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiming Chen ◽  
Liping Chen ◽  
Mohammad Shabaz
Keyword(s):  
Edge Detection ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Region ◽  
Experimental Comparison ◽  
Fusion Algorithm ◽  
High Frequency Region ◽  
Running Time ◽  
Low Efficiency

In the present scenario, image fusion is utilized at a large level for various applications. But, the techniques and algorithms are cumbersome and time-consuming. So, aiming at the problems of low efficiency, long running time, missing image detail information, and poor image fusion, the image fusion algorithm at pixel level based on edge detection is proposed. The improved ROEWA (Ratio of Exponentially Weighted Averages) operator is used to detect the edge of the image. The variable precision fitting algorithm and edge curvature change are used to extract the feature line of the image edge and edge angle point of the feature to improve the stability of image fusion. According to the information and characteristics of the high-frequency region and low-frequency region, different image fusion rules are set. To cope with the high-frequency area, the local energy weighted fusion approach based on edge information is utilized. The low-frequency region is processed by merging the region energy with the weighting factor, and the fusion results of the high findings demonstrate that the image fusion technique presented in this work increases the resolution by 1.23 and 1.01, respectively, when compared to the two standard approaches. When compared to the two standard approaches, the experimental results show that the proposed algorithm can effectively reduce the lack of image information. The sharpness and information entropy of the fused image are higher than the experimental comparison method, and the running time is shorter and has better robustness.

A Novel Adaptive PET/CT Image Fusion Algorithm

2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
High Frequency ◽  
Malignant Tumors ◽  
Low Frequency ◽  
Combination Method ◽  
Ct Image ◽  
Fusion Algorithm ◽  
Pet Ct ◽  
Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

Anderson-Stuart Model to Analyze AC and DC Conductivity of Lithium Zinc-Silicate Glass / Glass-Ceramics

2007 ◽  
Vol 280-283 ◽  
pp. 919-924
Author(s):  
M.S. Jogad ◽  
V.K. Shrikhande ◽  
A.H. Dyama ◽  
L.A. Udachan ◽  
Govind P. Kothiyal
Keyword(s):  
High Frequency ◽  
Glass Ceramics ◽  
Low Frequency ◽  
Frequency Region ◽  
Dc Conductivity ◽  
Frequency Range ◽  
High Frequency Region ◽  
Dc Conductivities ◽  
Conductivity Variation ◽  
Different Temperatures

AC and DC conductivities have been measured by using the real (e¢) and imaginary (e¢¢) parts of the dielectric constant data of glass and glass-ceramics (GC) at different temperatures in the rage 297-642K and in the frequency range 100 Hz to 10 MHz. Using Anderson –Stuart model, we have calculated the activation energy, which is observed to be lower than that of the DC conductivity. The analysis for glass/glass-ceramics indicates that the conductivity variation with frequency exhibits an initial linear region followed by nonlinear region with a maximum in the high-frequency region. The observed frequency dependence of ionic conductivity has been analyzed within the extended Anderson–Stuart model considering both the electrostatic and elastic strain terms. In glass/glassceramic the calculations based on the Anderson-Stuart model agree with the experimental observations in the low frequency region but at higher frequencies there is departure from measured data.

scholarly journals Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

2018 ◽  
Vol 7 (2.31) ◽  
pp. 165
Author(s):  
M Shyamala Devi ◽  
P Balamurugan
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Gabor Filter ◽  
Low Frequency ◽  
Local Energy ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Image Averaging

Image processing technology requires moreover the full image or the part of image which is to be processed from the user’s point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edge-like features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work .

Multisource Image Fusion Based on DWT and Simplified Pulse Coupled Neural Network

2013 ◽  
Vol 457-458 ◽  
pp. 736-740 ◽  
Author(s):  
Nian Yi Wang ◽  
Wei Lan Wang ◽  
Xiao Ran Guo
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Cortical Model ◽  
Fusion Algorithm ◽  
Global Coupling ◽  
Maximum Selection ◽  
Pulse Synchronization

In this paper, a new image fusion algorithm based on discrete wavelet transform (DWT) and spiking cortical model (SCM) is proposed. The multiscale decomposition and multi-resolution representation characteristics of DWT are associated with global coupling and pulse synchronization features of SCM. Two different fusion rules are used to fuse the low and high frequency sub-bands respectively. Maximum selection rule (MSR) is used to fuse low frequency coefficients. As to high frequency subband coefficients, spatial frequency (SF) is calculated and then imputed into SCM to motivate neural network. Experimental results demonstrate the effectiveness of the proposed fusion method.

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.

A Thin Cloud Removal Method for Optical Image Based on Improved Homomorphism Filtering

2014 ◽  
Vol 618 ◽  
pp. 519-522
Author(s):  
Guang Yu ◽  
Wen Bang Sun ◽  
Gang Liu ◽  
Mai Yu Zhou
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Frequency Component ◽  
Frequency Region ◽  
High Frequency Region ◽  
Filtering Method ◽  
Thin Cloud ◽  
Cloud Removal ◽  
Thin Cloud Removal ◽  
Image Definition

Optical remote image is affected by thin cloud inevitably, which debases image definition. Traditional homomorphism filtering frequently used in thin cloud removing has affect on the cloud in low frequency region, but is not effective for those in high frequency region. An improved homomorphism filtering method is proposed on the basis of statistical characters of image information. Instead of the filtering in frequency field, it isolates the low frequency component of the image representing cloud information with calculating neighborhood average in spatial field. Then, the filtered image is enhanced based on rough set. The experiment results show that the proposed method compared to traditional methods can obtain good results and performs faster.

Modeling of Perforated Screens or Membrane Using Rigid Frame Porous Models Combined with Thin Membrane Resonance Sound Absorbing Theory

2013 ◽  
Vol 357-360 ◽  
pp. 1206-1211
Author(s):  
Xiao Ling Gai ◽  
Xian Hui Li ◽  
Bin Zhang ◽  
Peng Xie ◽  
Zhi Hui Ma
Keyword(s):  
High Frequency ◽  
Sound Absorption ◽  
Mass Density ◽  
Low Frequency ◽  
Frequency Region ◽  
Thin Membrane ◽  
High Frequency Region ◽  
Rigid Frame ◽  
Membrane Resonance

The sound absorption ability of screen or perforated membrane is studied based on rigid frame porous models combined with thin membrane resonance sound absorbing theory in this paper. Results show that the sound absorption of screen or perforated membrane is better considering the role of membrane than using the rigid frame porous models when the mass density of screen or perforated membrane is smaller. The rigid frame porous model is very accuracy to model the sound absorption ability of screen or perforated membrane when the mass density of membrane is greater. The parameter studies present that the sound absorption peaks move toward low frequency region with the increasing of the depth of air-back cavity, mass density and thickness of screens or perforated membrane and moves toward high frequency region with the increasing of the perforation and perforated radius of screens or perforated membrane when other parameters keep invariant.

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.

Fusion Algorithm of Infrared and Visible Images Based on Multiresolution Decomposition

2012 ◽  
Vol 239-240 ◽  
pp. 229-232
Author(s):  
Chen Ding
Keyword(s):  
Statistical Model ◽  
Image Fusion ◽  
Frequency Band ◽  
High Frequency ◽  
Wavelet Coefficient ◽  
Low Frequency ◽  
High Frequency Band ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Multiresolution Decomposition

Information redundancy and complementarity are existing between the images obtained by multi-sensor, image fusion can improve the certainty and reliability of the information. Traditional method of image fusion based on multiresolution decomposition is susceptible to high frequency noise, fusion is often ineffective. A image fusion algorithm has been studied based on the wavelet multiresolution decomposition which is regional energy maximum for low-frequency decomposition image, and the bivariate statistical model for high-frequency part. The results show that: in the conditions of Daubechies 3 wavelet basis function, decomposition level 5 multiresolution decomposition, the bivariate statistical model for the high-frequency band is robust to noise based on the joint probability of wavelet coefficient pair - a wavelet coefficient and its parent; in the same time, the regional energy maximum for low-frequency band can be effective on the high-frequency band based on the bivariate statistical model. The fusion image has the biggish contrast, the preferable details, the higher gray level resolution.

On the Assignment and Directional Dispersion of Polar Phonon Modes in K3Cu(CN)4 and K3Ag(CN)4

1974 ◽  
Vol 29 (7) ◽  
pp. 1011-1016
Author(s):  
W. Nitsch ◽  
H. J. Falge ◽  
R. Claus
Keyword(s):  
Light Scattering ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Region ◽  
Longitudinal Vibrations ◽  
Phonon Modes ◽  
High Frequency Region ◽  
Single Crystalline ◽  
Scattering Measurements ◽  
Stretching Vibrations

Polar phonon modes in single crystalline K3Cu(CN)4 and K3Ag(CN)4 have been experimentally studied by light scattering. Measurements at 293, 82 and 6 K made possible assignments in the low frequency region from 0 to about 700 cm-1 and the high frequency region from 2030 to about 2100 cm-1 originating from the C≡N stretching vibrations. Directional dispersion due to the anisotropy of K3Cu(CN)4 has been studied and allowed the identification of the transversal and longitudinal vibrations in the high frequency region. IR-reflectivity measurements, partly on the basis of the ATR-method, have been used in order to support the assignments in this region.

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