An objective evaluation metric for color image fusion

2012 ◽  
Author(s):  
Wenjie Dong ◽  
Yufeng Zheng
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Objective Evaluation ◽  
Evaluation Metric

Objective evaluation of color image fusion methods

2009 ◽  
Vol 48 (6) ◽  
pp. 066201 ◽  
Author(s):  
Vassilis Tsagaris
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Objective Evaluation ◽  
Fusion Methods

Image Fusion Algorithm Based on Contrast Pyramid and its Performance Evaluation

2014 ◽  
Vol 525 ◽  
pp. 711-714 ◽  
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Jie Li
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Objective Evaluation ◽  
Image Simulation ◽  
Fusion Algorithm ◽  
Reconstruction Process ◽  
Creation Process ◽  
Focus Image ◽  
Simulation Results ◽  
Contrast Information

Contrast pyramid algorithm is put forward in this paper. The human visual system is sensitive to contrast information of image, so contrast pyramid algorithm would outstanding the contrast of image. The algorithm consists of creation process of Gauss Pyramid, the process of creating contrast Pyramid and reconstruction process of clear image. Simulation by MATLAB was completed in multi-focus image, multi-modality image and color image. Objective evaluation index such as mean, standard deviation, entropy and average gradient was calculated Simulation results and index show that the contrast pyramid algorithm has advantage of projecting the contrast of image, especially in color image fusion.

scholarly journals International Journal of Applied Mathematics and Machine Learning

2021 ◽  
Vol 15 (1) ◽  
pp. 1-29
Author(s):  
Xiuming Sun ◽  
◽  
Weina Wu ◽  
Peng Geng ◽  
Lin Lu ◽  
...  
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Color Image ◽  
Applied Mathematics ◽  
Objective Evaluation ◽  
Relevant Information ◽  
Computational Mathematics ◽  
Fused Image ◽  
Focus Image ◽  
Sparse Coefficient

In order to achieve the multi-focus image fusion task, a sparse representation method based on quaternion for multi-focus image fusion is proposed in this paper. Firstly, the RGB color information of each pixel in the color image is represented by quaternion based on the relevant knowledge of computational mathematics, and the color image pixel is processed as a whole vector to maintain the relevant information between the three color channels. Secondly, the dictionary represented by quaternion and the sparse coefficient represented by quaternion are obtained by using the our proposed sparse representation model. Thirdly, the coefficient fusion is carried out by using the “max-L1” rule. Finally, the fused sparse coefficient and dictionary are used for image reconstruction to obtain the quaternion fused image, which is then converted into RGB color multi-focus fused image. Our method belongs to computational mathematics, and uses the relevant knowledge in the field of computational mathematics to help us carry out the experiment. The experimental results show that the method has achieved good results in visual quality and objective evaluation.

scholarly journals Infrared and visible image fusion via octave Gaussian pyramid framework

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Yan ◽  
Qun Hao ◽  
Jie Cao ◽  
Rizvi Saad ◽  
Kun Li ◽  
...  
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Visible Image ◽  
Composite Image ◽  
Multiscale Decomposition ◽  
Gaussian Pyramid ◽  
Fusion Methods ◽  
Scale Spaces ◽  
Fusion Framework

AbstractImage fusion integrates information from multiple images (of the same scene) to generate a (more informative) composite image suitable for human and computer vision perception. The method based on multiscale decomposition is one of the commonly fusion methods. In this study, a new fusion framework based on the octave Gaussian pyramid principle is proposed. In comparison with conventional multiscale decomposition, the proposed octave Gaussian pyramid framework retrieves more information by decomposing an image into two scale spaces (octave and interval spaces). Different from traditional multiscale decomposition with one set of detail and base layers, the proposed method decomposes an image into multiple sets of detail and base layers, and it efficiently retains high- and low-frequency information from the original image. The qualitative and quantitative comparison with five existing methods (on publicly available image databases) demonstrate that the proposed method has better visual effects and scores the highest in objective evaluation.

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.

A robust color image fusion for low light level and infrared images

2016 ◽  
Author(s):  
Chao Liu ◽  
Xiao-hui Zhang ◽  
Qing-ping Hu ◽  
Yong-kang Chen
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Light Level ◽  
Infrared Images ◽  
Low Light

Fine grained medical image fusion using type-2 fuzzy logic

2019 ◽  
Vol 14 (2) ◽  
pp. 999
Author(s):  
Ramya H.R ◽  
B K Sujatha
Keyword(s):  
Fuzzy Logic ◽  
Image Fusion ◽  
Medical Image ◽  
Objective Evaluation ◽  
Medical Image Fusion ◽  
Fine Grained ◽  
Sharp Feature ◽  
Sugeno Model ◽  
Image Fusion Technique

<p>In recent years, many fast-growing technologies coupled with wide volume of medical data for the digitalization of that data. Thus, researchers have shown their immense interest on Multi-sensor image fusion technologies which convey image information based on data from various sensor modalities into a single image. The image fusion technique is a widespread technique for the diagnosis of medical instrumentation and measurement. Therefore, in this paper we have introduced a novel multimodal sensor medical image fusion method based on type-2 fuzzy logic is proposed using Sugeno model. Moreover, a Gaussian smoothen filter is introduced to extract the detailed information of an image using sharp feature points.Type-2 fuzzy algorithm is used to achieve highly efficient feature points from both the b images to provide high visually classified resultant image. The experimental results demonstrate that the proposed method can achieve better performance than the state-of-the- art methods in terms of visual quality and objective evaluation.</p>

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