The Experts below are selected from a list of 39621 Experts worldwide ranked by ideXlab platform
Jianwen Hu - One of the best experts on this subject based on the ideXlab platform.
-
pixel level Image Fusion
Information Fusion, 2017Co-Authors: Shutao Li, Xudong Kang, Leyuan Fang, Jianwen HuAbstract:This review provides a survey of various pixel-level Image Fusion methods according to the adopted transform strategy.The existing Fusion performance evaluation methods and the unresolved problems are concluded.The major challenges met in different Image Fusion applications are analyzed and concluded. Pixel-level Image Fusion is designed to combine multiple input Images into a fused Image, which is expected to be more informative for human or machine perception as compared to any of the input Images. Due to this advantage, pixel-level Image Fusion has shown notable achievements in remote sensing, medical imaging, and night vision applications. In this paper, we first provide a comprehensive survey of the state of the art pixel-level Image Fusion methods. Then, the existing Fusion quality measures are summarized. Next, four major applications, i.e., remote sensing, medical diagnosis, surveillance, photography, and challenges in pixel-level Image Fusion applications are analyzed. At last, this review concludes that although various Image Fusion methods have been proposed, there still exist several future directions in different Image Fusion applications. Therefore, the researches in the Image Fusion field are still expected to significantly grow in the coming years.
Janet E Nichol - One of the best experts on this subject based on the ideXlab platform.
-
wavelet based Image Fusion and quality assessment
International Journal of Applied Earth Observation and Geoinformation, 2005Co-Authors: Yan Tian, Janet E NicholAbstract:Abstract Recent developments in satellite and sensor technologies have provided high-resolution satellite Images. Image Fusion techniques can improve the quality, and increase the application of these data. This paper addresses two issues in Image Fusion (a) the Image Fusion method and (b) corresponding quality assessment. Firstly, a multi-band wavelet-based Image Fusion method is presented, which is a further development of the two-band wavelet transformation. This Fusion method is then applied to a case study to demonstrate its performance in Image Fusion. Secondly, quality assessment for fused Images is discussed. The objectives of Image Fusion include enhancing the visibility of the Image and improving the spatial resolution and the spectral information of the original Images. For assessing quality of an Image after Fusion, we define the aspects to be assessed initially. These include, for instance, spatial and spectral resolution, quantity of information, visibility, contrast, or details of features of interest. Quality assessment is application dependant; different applications may require different aspects of Image quality. Based on this analysis, a set of qualities is classified and analyzed. These sets of qualities include (a) average grey value, for representing intensity of an Image, (b) standard deviation, information entropy, profile intensity curve for assessing details of fused Images, and (c) bias and correlation coefficient for measuring distortion between the original Image and fused Image in terms of spectral information.
Jesus M De La Cruz - One of the best experts on this subject based on the ideXlab platform.
-
a wavelet based Image Fusion tutorial
Pattern Recognition, 2004Co-Authors: Gonzalo Pajares, Jesus M De La CruzAbstract:Abstract The objective of Image Fusion is to combine information from multiple Images of the same scene. The result of Image Fusion is a new Image which is more suitable for human and machine perception or further Image-processing tasks such as segmentation, feature extraction and object recognition. Different Fusion methods have been proposed in literature, including multiresolution analysis. This paper is an Image Fusion tutorial based on wavelet decomposition, i.e. a multiresolution Image Fusion approach. We can fuse Images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based Image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some Image Fusion examples illustrate the proposed Fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.
Shutao Li - One of the best experts on this subject based on the ideXlab platform.
-
pixel level Image Fusion
Information Fusion, 2017Co-Authors: Shutao Li, Xudong Kang, Leyuan Fang, Jianwen HuAbstract:This review provides a survey of various pixel-level Image Fusion methods according to the adopted transform strategy.The existing Fusion performance evaluation methods and the unresolved problems are concluded.The major challenges met in different Image Fusion applications are analyzed and concluded. Pixel-level Image Fusion is designed to combine multiple input Images into a fused Image, which is expected to be more informative for human or machine perception as compared to any of the input Images. Due to this advantage, pixel-level Image Fusion has shown notable achievements in remote sensing, medical imaging, and night vision applications. In this paper, we first provide a comprehensive survey of the state of the art pixel-level Image Fusion methods. Then, the existing Fusion quality measures are summarized. Next, four major applications, i.e., remote sensing, medical diagnosis, surveillance, photography, and challenges in pixel-level Image Fusion applications are analyzed. At last, this review concludes that although various Image Fusion methods have been proposed, there still exist several future directions in different Image Fusion applications. Therefore, the researches in the Image Fusion field are still expected to significantly grow in the coming years.
Xia Ming - One of the best experts on this subject based on the ideXlab platform.
-
A survey on multisensor Image Fusion
Electronics Optics & Control, 2020Co-Authors: Xia MingAbstract:Multisensor Image Fusion is widely recognized as a valuable mechanism for improving overall system performance in Image based application areas. The objective of Image Fusion is to combine information from multiple Images of the same scene to achieve inferences that cannot be achieved with a single Image or source. This paper surveys pixel, feature, and symbol level Image Fusion methods and compares the characteristics of different Fusion levels. A few Image Fusion algorithms of each level are presented, problems about Fusion of infrared Image, VI, SAR, multispectral Image are discussed. An Image Fusion structure model with feedback information and some issues that should be solved in the future are proposed in this paper.
-
Overview of wavelet-analysis-based Image Fusion
Infrared and Laser Engineering, 2020Co-Authors: Xia MingAbstract:The objective of Image Fusion is to combine information from multiple Images of the same scenc to accomplish tasks that cannot be achieved with a single Image or source. Wavelets with their multiresolution property, have been proved to be effective in the integration of the coarse features and finer resolution details of these Images to produce a well fused Image. This paper surveys methods and principle of wavelet\|based Image Fusion, pays attention to advantages of wavelet\|based Image Fusion, and compares the characteristics of different Image Fusion rules. Furthermore, wavelet\|packet\|based, wavelet\|frame\|based and multiwavelet\|based Image Fusion methods are presented. \;
