A Novel Image Fusion Approach for Underwater Blurred Images

2013 ◽  
Vol 427-429 ◽  
pp. 1807-1812
Author(s):  
Ming Wei Sheng ◽  
Yong Jie Pang ◽  
Hai Huang ◽  
Tie Dong Zhang
Keyword(s):  
Image Fusion ◽  
Vision System ◽  
Median Filter ◽  
Low Frequency ◽  
Biorthogonal Wavelet ◽  
Underwater Image ◽  
High Quality Image ◽  
Fused Image ◽  
Blurred Images ◽  
Fusion Scheme

The main purpose of underwater image fusion is to combine multi-images about the same object into a high-quality image with abundant information. A new underwater image fusion scheme based on Biorthogonal wavelet transform was presented, which is suitable to underwater computer vision system of AUV. Firstly, median filter algorithm was involved for improving the quality and contrast of two source underwater blurred images. Secondly, the different-position-focused underwater images were decomposed by Biorthogonal wavelet and the wavelet coefficients were acquired for reconstructing the fusion image. Finally, the fused image was constructed using the low-frequency and high-frequency domain fusion rules. By adopting a series of experiments for the underwater images fusion, an integrated underwater image with visible outline and distinguishable inner details was obtained.

A Novel Image Fusion Approach Based on Rough Set and Wavelet Analysis

2010 ◽  
Vol 439-440 ◽  
pp. 1069-1074 ◽  
Author(s):  
Zhi Yong Zhu
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Rough Set ◽  
Wavelet Coefficients ◽  
Absolute Value ◽  
High Quality Image ◽  
Quality Image ◽  
Fused Image ◽  
Fusion Scheme ◽  
Fusion Approach

The goal of image fusion is to combine a high-quality image from multi-image about the same object. The paper presents an image fusion scheme based on wavelet transform and rough set. Firstly, the two images are decomposed by orthogonal wavelet; the image’s wavelet coefficients are got. Comparing with the two image’s wavelet coefficients, wavelet coefficients’ matrix is composed of maximum absolute value, the fused image is obtained by the inverse wavelet transform. The last section of the paper verifies the method by experiment and gets the good experimental results.

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.

scholarly journals Multimodal Medical Image Fusion using Guided Filter in NSCT Domain

2018 ◽  
Vol 11 (4) ◽  
pp. 1937-1946
Author(s):  
Nancy Mehta ◽  
Sumit Budhiraja
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Similarity Index ◽  
Low Frequency ◽  
Structural Similarity ◽  
Relevant Information ◽  
Phase Congruency ◽  
Edge Preserving ◽  
Medical Image Fusion ◽  
Fused Image

Multimodal medical image fusion aims at minimizing the redundancy and collecting the relevant information using the input images acquired from different medical sensors. The main goal is to produce a single fused image having more information and has higher efficiency for medical applications. In this paper modified fusion method has been proposed in which NSCT decomposition is used to decompose the wavelet coefficients obtained after wavelet decomposition. NSCT being multidirectional,shift invariant transform provide better results.Guided filter has been used for the fusion of high frequency coefficients on account of its edge preserving property. Phase congruency is used for the fusion of low frequency coefficients due to its insensitivity to illumination contrast hence making it suitable for medical images. The simulated results show that the proposed technique shows better performance in terms of entropy, structural similarity index, Piella metric. The fusion response of the proposed technique is also compared with other fusion approaches; proving the effectiveness of the obtained fusion results.

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 .

Multifocus image fusion using multiscale transform and convolutional sparse representation

2020 ◽  
pp. 2050061
Author(s):  
Chengfang Zhang
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Low Frequency ◽  
Activity Level ◽  
Source Image ◽  
Level Measurement ◽  
Fused Image ◽  
Fusion Methods ◽  
Low Pass ◽  
Multiscale Transform

Multifocus image fusion can obtain an image with all objects in focus, which is beneficial for understanding the target scene. Multiscale transform (MST) and sparse representation (SR) have been widely used in multifocus image fusion. However, the contrast of the fused image is lost after multiscale reconstruction, and fine details tend to be smoothed for SR-based fusion. In this paper, we propose a fusion method based on MST and convolutional sparse representation (CSR) to address the inherent defects of both the MST- and SR-based fusion methods. MST is first performed on each source image to obtain the low-frequency components and detailed directional components. Then, CSR is applied in the low-pass fusion, while the high-pass bands are fused using the popular “max-absolute” rule as the activity level measurement. The fused image is finally obtained by performing inverse MST on the fused coefficients. The experimental results on multifocus images show that the proposed algorithm exhibits state-of-the-art performance in terms of definition.

IMPROVED CT-MR IMAGE FUSION SCHEME USING DUAL TREE COMPLEX CONTOURLET TRANSFORM BASED ON PCA

2010 ◽  
Vol 07 (02) ◽  
pp. 99-107 ◽  
Author(s):  
NEMIR AL-AZZAWI ◽  
WAN AHMED K. WAN ABDULLAH
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Image Data ◽  
Frequency Component ◽  
Contourlet Transform ◽  
Ct Image ◽  
Directional Information ◽  
Fused Image

Medical image fusion has been used to derive useful information from multimodality medical image data. This paper presents a dual-tree complex contourlet transform (DT-CCT) based approach for the fusion of magnetic resonance image (MRI) and computed tomography (CT) image. The objective of the fusion of an MRI and a CT image of the same organ is to obtain a single image containing as much information as possible about that organ for diagnosis. The limitation of directional information of dual-tree complex wavelet (DT-CWT) is rectified in DT-CCT by incorporating directional filter banks (DFB) into the DT-CWT. To improve the fused image quality, we propose a new method for fusion rules based on the principle component analysis (PCA) which depend on frequency component of DT-CCT coefficients (contourlet domain). For low frequency coefficients, PCA method is adopted and for high frequency coefficients, the salient features are picked up based on local energy. The final fusion image is obtained by directly applying inverse dual tree complex contourlet transform (IDT-CCT) to the fused low and high frequency coefficients. The DT-CCT produces images with improved contours and textures, while the property of shift invariance is retained. The experimental results showed that the proposed method produces fixed image with extensive features on multimodality.

Wavelet Transform with a Novel Integration Technique for Image Fusion

2011 ◽  
Vol 204-210 ◽  
pp. 1419-1422 ◽  
Author(s):  
Yong Yang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Experimental Results ◽  
Integration Technique ◽  
Frequency Bands ◽  
Fusion Methods ◽  
Fusion Scheme ◽  
Measures Of Performance

Image fusion is to combine several different source images to form a new image by using a certain method. Recent studies show that among a variety of image fusion algorithms, the wavelet-based method is more effective. In the wavelet-based method, the key technique is the fusion scheme, which can decide the final fused result. This paper presents a novel fusion scheme that integrates the wavelet decomposed coefficients in a quite separate way when fusing images. The method is formed by considering the different physical meanings of the coefficients in both the low frequency and high frequency bands. The fused results were compared with several existing fusion methods and evaluated by three measures of performance. The experimental results can demonstrate that the proposed method can achieve better performance than conventional image fusion methods.

scholarly journals Image Fusion Based Multimodal Biometric Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 3613-3617
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
The Body ◽  
Biometric Authentication ◽  
Biometric Data ◽  
Ringing Artifacts ◽  
Fused Image ◽  
Focus Image ◽  
Multimodal Biometric Recognition ◽  
Authentic Data

Biometric Authentication is a security process that replays on the unique biological characteristics of an individual. Biometric Authentication system compare a biometric data capture to stored, confirmed authentic data in a database. It is simply the process of verifying the identity using the measurements or other unique characteristics of the body, then logging us in a service, device and so on. It is an effective way to prove identity because it can’t be replicated. Multi focus Image fusion is a process of fusing two or more images to obtain a new one. Used to reduce the problems like blocking, ringing artifacts occurs because of DCT. The low frequency sub-band coefficients are fused by selecting coefficient having maximum spatial frequency. The goal is classifying the images to classes of authorized and unauthorized using multi class SVM. The fingerprint image and iris image are fused together using SWT, the features are extracted from the fused image and labelled using GLCM algorithm. The testing image is then compared with trained samples and classified as authorized or unauthorized by using FFNN.

scholarly journals Medical image fusion based on quaternion wavelet transform

2020 ◽  
Vol 14 ◽  
pp. 174830262093129
Author(s):  
Zhang Zhancheng ◽  
Luo Xiaoqing ◽  
Xiong Mengyu ◽  
Wang Zhiwen ◽  
Li Kai
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Frequency Component ◽  
High Frequency Component ◽  
Medical Image Fusion ◽  
Quaternion Wavelet Transform ◽  
Fused Image

Medical image fusion can combine multi-modal images into an integrated higher-quality image, which can provide more comprehensive and accurate pathological information than individual image does. Traditional transform domain-based image fusion methods usually ignore the dependencies between coefficients and may lead to the inaccurate representation of source image. To improve the quality of fused image, a medical image fusion method based on the dependencies of quaternion wavelet transform coefficients is proposed. First, the source images are decomposed into low-frequency component and high-frequency component by quaternion wavelet transform. Then, a clarity evaluation index based on quaternion wavelet transform amplitude and phase is constructed and a contextual activity measure is designed. These measures are utilized to fuse the high-frequency coefficients and the choose-max fusion rule is applied to the low-frequency components. Finally, the fused image can be obtained by inverse quaternion wavelet transform. The experimental results on some brain multi-modal medical images demonstrate that the proposed method has achieved advanced fusion result.

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