scholarly journals A Fusion Method for Atomic Force Acoustic Microscopy Cell Imaging Based on Local Variance in Non-Subsampled Shearlet Transform Domain

2020 ◽  
Vol 10 (21) ◽  
pp. 7424
Author(s):  
Pengxin Cao ◽  
Xiaoqing Li ◽  
Mingyue Ding
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Acoustic Microscopy ◽  
Source Image ◽  
Fusion Method ◽  
Cell Region ◽  
Shearlet Transform ◽  
Local Variance ◽  
Atomic Force ◽  
Internal Structures

Atomic force acoustic microscopy (AFAM) is a measurement method that uses the probe and acoustic wave to image the surface and internal structures of different materials. For cellular material, the morphology and phase images of AFAM reflect the outer surface and internal structures of the cell, respectively. This paper proposes an AFAM cell image fusion method in the Non-Subsampled Shearlet Transform (NSST) domain, based on local variance. First, NSST is used to decompose the source images into low-frequency and high-frequency sub-bands. Then, the low-frequency sub-band is fused by the weight of local variance, while a contrast limited adaptive histogram equalization is used to improve the source image contrast to better express the details in the fused image. The high-frequency sub-bands are fused using the maximum rule. Since the AFAM image background contains a lot of noise, and improved segmentation algorithm based on the Otsu algorithm is proposed to segment the cell region, and the image quality metrics based on the segmented region will make the evaluation more accurate. Experiments with different groups of AFAM cell images demonstrated that the proposed method can clearly show the internal structures and the contours of the cells, compared with traditional methods.

scholarly journals An Effective Nonlocal Means Image Denoising Framework Based On Nonsubsampled Shearlet Transform

2021 ◽  
Author(s):  
Bhawna Goyal ◽  
Ayush Dogra ◽  
Arun Kumar Sangaiah
Keyword(s):  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Processing System ◽  
Subjective Assessment ◽  
Source Image ◽  
Nonlocal Means ◽  
Shearlet Transform ◽  
Influence Of Noise ◽  
Denoised Image

Abstract Image denoising is a fundamental task in computer vision and image processing system with an aim of estimating the original image by eliminating the noise and artifact from the noise-corrupted version of the image. In this study, a nonlocal means (NLM) algorithm with NSST (non-subsampled shearlet transform) has been designed to surface a computationally simple image denoising algorithm. There are three steps in our process; First, NSST is employed to decompose source image into coarser and finer layers. The number of decomposition level of NSST is set to two, resulting in one low frequency coefficient (coarser layer) and four high frequency coefficients (finer layers). The two levels of decomposition are used in order to preserve memory, reduce processing time, and reduce the influence of noise and misregistration errors. The finer layers are then processed using NLM algorithm, while the coarser layer is left as it is. The NL-Means algorithm reduces noise in finer layers while maintaining the sharpness of strong edges, such as the image silhouette. When compared to noisy images, this filter also smoothes textured regions, resulting in retaining more information. To obtain a final denoised image, inverse NSST is performed to the coarser layer and the NL-means filtered finer layers. The robustness of our method has been tested on the different multisensor and medical image dataset with diverse levels of noise. In the context of both subjective assessment and objective measurement, our method outperforms numerous other existing denoising algorithms notably in terms of retaining fine image structures. It is also clearly exhibited that the proposed method is computationally more effective as compared to other prevailing algorithms.

scholarly journals Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ling Tan ◽  
Xin Yu
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Medical Image ◽  
Information Transfer ◽  
Medical Images ◽  
Low Frequency ◽  
Source Image ◽  
Fusion Algorithm ◽  
Shearlet Transform

Clinical diagnosis has high requirements for the visual effect of medical images. To obtain rich detail features and clear edges for fusion medical images, an image fusion algorithm FFST-SR-PCNN based on fast finite shearlet transform (FFST) and sparse representation is proposed, aiming at the problem of poor clarity of edge details that is conducive to maintaining the details of source image in current algorithms. Firstly, the source image is decomposed into low-frequency coefficients and high-frequency coefficients by FFST. Secondly, the K-SVD method is used to train the low-frequency coefficients to obtain the overcomplete dictionary D, and then the OMP algorithm sparsely encodes the low-frequency coefficients to complete the fusion of the low-frequency coefficients. Then, a high-frequency coefficient is applied to excite a pulse-coupled neural network, and the fusion coefficient of the high-frequency coefficient is selected according to the number of ignitions. Finally, the fused low-frequency coefficient and high-frequency coefficient are reconstructed into the fused medical image by FFST inverse transform. The experimental results show that the image fusion result of the proposed algorithm is about 35% higher than the comparison algorithms for the edge information transfer factor QAB/F index and has achieved good results in both subjective visual effects and objective evaluation indicators.

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.

scholarly journals Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1362
Author(s):  
Hui Wan ◽  
Xianlun Tang ◽  
Zhiqin Zhu ◽  
Weisheng Li
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Fusion Method ◽  
Complementary Information ◽  
Multi Scale ◽  
Initial Decision ◽  
Focus Image ◽  
Frequency Components ◽  
Image Fusion Method

Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods.

scholarly journals An Image Fusion Method Based on Curvelet Transform and Guided Filter Enhancement

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Zhang ◽  
Xu Ma ◽  
Yanshan Tian
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Curvelet Transform ◽  
Night Vision ◽  
Guided Filter ◽  
Source Image ◽  
Inverse Transformation ◽  
Fusion Method ◽  
Visible Image ◽  
Coupled Neural Networks

In order to improve the clarity of image fusion and solve the problem that the image fusion effect is affected by the illumination and weather of visible light, a fusion method of infrared and visible images for night-vision context enhancement is proposed. First, a guided filter is used to enhance the details of the visible image. Then, the enhanced visible and infrared images are decomposed by the curvelet transform. The improved sparse representation is used to fuse the low-frequency part, while the high-frequency part is fused with the parametric adaptation pulse-coupled neural networks. Finally, the fusion result is obtained by inverse transformation of the curvelet transform. The experimental results show that the proposed method has good performance in detail processing, edge protection, and source image information.

scholarly journals Medical Image Fusion Based on Sparse Representation and PCNN in NSCT Domain

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jingming Xia ◽  
Yiming Chen ◽  
Aiyue Chen ◽  
Yicai Chen
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Medical Image ◽  
Information Transfer ◽  
Color Image ◽  
Low Frequency ◽  
Source Image ◽  
Medical Image Fusion

The clinical assistant diagnosis has a high requirement for the visual effect of medical images. However, the low frequency subband coefficients obtained by the NSCT decomposition are not sparse, which is not conducive to maintaining the details of the source image. To solve these problems, a medical image fusion algorithm combined with sparse representation and pulse coupling neural network is proposed. First, the source image is decomposed into low and high frequency subband coefficients by NSCT transform. Secondly, the K singular value decomposition (K-SVD) method is used to train the low frequency subband coefficients to get the overcomplete dictionary D, and the orthogonal matching pursuit (OMP) algorithm is used to sparse the low frequency subband coefficients to complete the fusion of the low frequency subband sparse coefficients. Then, the pulse coupling neural network (PCNN) is excited by the spatial frequency of the high frequency subband coefficients, and the fusion coefficients of the high frequency subband coefficients are selected according to the number of ignition times. Finally, the fusion medical image is reconstructed by NSCT inverter. The experimental results and analysis show that the algorithm of gray and color image fusion is about 34% and 10% higher than the contrast algorithm in the edge information transfer factor QAB/F index, and the performance of the fusion result is better than the existing algorithm.

A New Image Fusion Method Based on Improved PCNN and Multiscale Decomposition

2013 ◽  
Vol 834-836 ◽  
pp. 1011-1015 ◽  
Author(s):  
Nian Yi Wang ◽  
Wei Lan Wang ◽  
Xiao Ran Guo
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Fusion Rule ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Cortical Model ◽  
Fusion Algorithm ◽  
Maximum Selection

A new image fusion algorithm based on nonsubsampled contourlet transform and spiking cortical model is proposed in this paper. Considering the human visual system characteristics, two different fusion rules are used to fuse the low and high frequency sub-bands of nonsubsampled contourlet transform respectively. A new maximum selection rule is defined to fuse low frequency coefficients. Spatial frequency is used for the fusion rule of high frequency coefficients. Experimental results demonstrate the effectiveness of the proposed fusion method.

An Image Fusion Method Based on the Combination of Lifting Wavelet and Median Filter

2014 ◽  
Vol 530-531 ◽  
pp. 394-402
Author(s):  
Ze Tao Jiang ◽  
Li Wen Zhang ◽  
Le Zhou
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Median Filter ◽  
Low Frequency ◽  
Weighted Average ◽  
Fusion Rule ◽  
Fusion Method ◽  
Frequency Scale ◽  
Lifting Wavelet ◽  
Image Fusion Method

At present, image fusion universally exists problem that fuzzy edge, sparse texture. To solve this problem, this study proposes an image fusion method based on the combination of Lifting Wavelet and Median Filter. The method adopts different fusion rules. For the low frequency coefficient, the low frequency scale coefficients have had the convolution do the square respectively to get enhanced edge of the image fusion. Then the details information of original image is extracted by measuring region characteristics. For high frequency coefficient, the high frequency parts are denoised by the Median Filter, and then neighborhood spatial frequency and consistency verification fusion rule is adopted to the fusion of detail sub-images. Compared with Weighted Average and Regional Energy , experimental results show that edge and texture information are the most. Method in study solves the fuzzy edge and sparse texture in a certain degree,which has strong practical value in image fusion.

scholarly journals Warp-knitted Fabric Defect Segmentation Based on the Shearlet Transform

2017 ◽  
Vol 25 (0) ◽  
pp. 87-94
Author(s):  
Zhijia Dong ◽  
Dong Xia ◽  
Pibo Ma ◽  
Gaoming Jiang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Curvelet Transform ◽  
High Energy ◽  
Knitted Fabric ◽  
Threshold Method ◽  
Shearlet Transform ◽  
Fabric Defect ◽  
Polar Grid ◽  
Warp Knitted Fabric

The Shearlet transform has been a burgeoning method applied in the area of image processing recently which, differing from the Wavelet transform, has excellent properties in processing singularities for multidimensional signals. Not only is it similar to the performance of the Curvelet transform, it also overcomes the disadvantage of the Curvelet transform with respect to discretization. In this paper, the Shearlet transform with segmented threshold de-nosing is proposed to segment a warp-knitted fabric defect. Firstly a warp-knitted fabric image of size 512*512 is filtered by the Laplacian Pyramid transform and decomposed into low frequency and high frequency coefficients. Secondly the high frequency coefficients are operated with a pseudo-polar grid and then convoluted by the window function. Thirdly the shearlet coefficients will be obtained through redefining the Cartesian coordinates from the pseudo-polar grid coordinates and de-noised by the segmented threshold method. Then the coefficients which have high energy are selected for reconstruction in an inverse way using the previous steps. Finally the iterative threshold method and object operation based on morphology are applied to segment out the defect profile. The experiment’s result states that the Shearlet transform shows excellent performance in segmenting a common warp-knitted fabric defect, indicating that the segment results can be applied for further defect automatic recognition.

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