Multi-modal Image Fusion Algorithm based on Variable Parameter Fractional Difference Enhancement

2020 ◽  
Author(s):  
Lei Zhang ◽  
Linna Ji ◽  
Hualong Jiang ◽  
Fengbao Yang ◽  
Xiaoxia Wang
Keyword(s):  
Image Fusion ◽  
Variable Parameter ◽  
Weighted Average ◽  
Fusion Algorithm ◽  
Fractional Difference ◽  
Contrast Enhanced ◽  
Fused Image ◽  
Fusion Images ◽  
The Difference

Multi-modal image fusion can more accurately describe the features of a scene than a single image. Because of the different imaging mechanisms, the difference between multi-modal images is great, which leads to poor contrast of the fused images. Therefore, a simple and effective spatial domain fusion algorithm based on variable parameter fractional difference enhancement is proposed. Based on the characteristics of fractional difference enhancement, a variable parameter fractional difference is introduced, the multi-modal images are repeatedly enhanced, and multiple enhanced images are obtained. A correlation coefficient is applied to constrain the number of enhancement cycles. In addition, an energy contrast is used to extract the contrast features of the image, and the tangent function is simultaneously used to obtain the fusion weight to attain multiple contrast-enhanced initialization fusion images. Finally, the weighted average is applied to obtain the final fused image. Experimental results demonstrate that the proposed fusion algorithm can effectively preserve the contrast features between images and improve the quality of fused images.

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.

Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform

2013 ◽  
Vol 401-403 ◽  
pp. 1381-1384 ◽  
Author(s):  
Zi Juan Luo ◽  
Shuai Ding
Keyword(s):  
Image Fusion ◽  
Multiple Scales ◽  
Weighted Average ◽  
Fusion Rule ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Focus Image ◽  
Limited Depth

t is mostly difficult to get an image that contains all relevant objects in focus, because of the limited depth-of-focus of optical lenses. The multifocus image fusion method can solve the problem effectively. Nonsubsampled Contourlet transform has varying directions and multiple scales. When the Nonsubsampled contourlet transform is introduced to image fusion, the characteristics of original images are taken better and more information for fusion is obtained. A new method of multi-focus image fusion based on Nonsubsampled contourlet transform (NSCT) with the fusion rule of region statistics is proposed in this paper. Firstly, different focus images are decomposed using Nonsubsampled contourlet transform. Then low-bands are integrated using the weighted average, high-bands are integrated using region statistics rule. Next the fused image will be obtained by inverse Nonsubsampled contourlet transform. Finally the experimental results are showed and compared with those of method based on Contourlet transform. Experiments show that the approach can achieve better results than the method based on contourlet transform.

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.

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.

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques

2021 ◽  
Author(s):  
Anuyogam Venkataraman
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Region Of Interest ◽  
Source Image ◽  
High Dose ◽  
Fusion Algorithm ◽  
Quantitative Metrics ◽  
Fused Image ◽  
Fusion Methods ◽  
Computed Tomography Image

With the increasing utilization of X-ray Computed Tomography (CT) in medical diagnosis, obtaining higher quality image with lower exposure to radiation is a highly challenging task in image processing. Sparse representation based image fusion is one of the sought after fusion techniques among the current researchers. A novel image fusion algorithm based on focused vector detection is proposed in this thesis. Firstly, the initial fused vector is acquired by combining common and innovative sparse components of multi-dosage ensemble using Joint Sparse PCA fusion method utilizing an overcomplete dictionary trained using high dose images of the same region of interest from different patients. And then, the strongly focused vector is obtained by determining the pixels of low dose and medium dose vectors which have high similarity with the pixels of the initial fused vector using certain quantitative metrics. Final fused image is obtained by denoising and simultaneously integrating the strongly focused vector, initial fused vector and source image vectors in joint sparse domain thereby preserving the edges and other critical information needed for diagnosis. This thesis demonstrates the effectiveness of the proposed algorithms when experimented on different images and the qualitative and quantitative results are compared with some of the widely used image fusion methods.

scholarly journals DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion

2020 ◽  
Author(s):  
Zixiang Zhao ◽  
Shuang Xu ◽  
Chunxia Zhang ◽  
Junmin Liu ◽  
Jiangshe Zhang ◽  
...  
Keyword(s):  
Image Fusion ◽  
Feature Maps ◽  
Visible Image ◽  
Qualitative And Quantitative ◽  
Fused Image ◽  
Fusion Images ◽  
Quantitative Results ◽  
Deep Image ◽  
Core Idea ◽  
Background Detail

Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images. This paper proposes a novel auto-encoder (AE) based fusion network. The core idea is that the encoder decomposes an image into background and detail feature maps with low- and high-frequency information, respectively, and that the decoder recovers the original image. To this end, the loss function makes the background/detail feature maps of source images similar/dissimilar. In the test phase, background and detail feature maps are respectively merged via a fusion module, and the fused image is recovered by the decoder. Qualitative and quantitative results illustrate that our method can generate fusion images containing highlighted targets and abundant detail texture information with strong reproducibility and meanwhile surpass state-of-the-art (SOTA) approaches.

Medical Image Fusion in Wavelet and Ridgelet Domains

2017 ◽  
pp. 711-723
Author(s):  
Vikrant Bhateja ◽  
Abhinav Krishn ◽  
Himanshi Patel ◽  
Akanksha Sahu
Keyword(s):  
Image Fusion ◽  
Information Content ◽  
Medical Image ◽  
Similarity Index ◽  
Principal Component ◽  
Structural Similarity ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Multi Scale ◽  
Fused Image

Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of life threatening diseases. Fusion has been performed using various approaches such as Pyramidal, Multi-resolution, multi-scale etc. Each and every approach of fusion depicts only a particular feature (i.e. the information content or the structural properties of an image). Therefore, this paper presents a comparative analysis and evaluation of multi-modal medical image fusion methodologies employing wavelet as a multi-resolution approach and ridgelet as a multi-scale approach. The current work tends to highlight upon the utility of these approaches according to the requirement of features in the fused image. Principal Component Analysis (PCA) based fusion algorithm has been employed in both ridgelet and wavelet domains for purpose of minimisation of redundancies. Simulations have been performed for different sets of MR and CT-scan images taken from ‘The Whole Brain Atlas'. The performance evaluation has been carried out using different parameters of image quality evaluation like: Entropy (E), Fusion Factor (FF), Structural Similarity Index (SSIM) and Edge Strength (QFAB). The outcome of this analysis highlights the trade-off between the retrieval of information content and the morphological details in finally fused image in wavelet and ridgelet domains.

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 .

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.

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