Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators

The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion.

[1]  Cedric Nishan Canagarajah,et al.  Region-Based Multimodal Image Fusion Using ICA Bases , 2007, IEEE Sensors Journal.

[2]  Jason Jianjun Gu,et al.  Multi-focus image fusion using PCNN , 2010, Pattern Recognit..

[3]  Alexander Toet,et al.  Towards cognitive image fusion , 2010, Inf. Fusion.

[4]  Victor Alchanatis,et al.  Image fusion of visible and thermal images for fruit detection. , 2009 .

[5]  A. Bonaert.  Introduction to the theory of Fuzzy subsets , 1977, Proceedings of the IEEE.

[6]  Rick S. Blum,et al.  Theoretical analysis of an information-based quality measure for image fusion , 2008, Inf. Fusion.

[7]  Rui Lai,et al.  A quantitative measure based infrared image enhancement algorithm using plateau histogram , 2010 .

[8]  Stavros G Demos,et al.  Multimodal near infrared spectral imaging as an exploratory tool for dysplastic esophageal lesion identification. , 2006, Optics express.

[9]  Xiangzhi Bai,et al.  Analysis of new top-hat transformation and the application for infrared dim small target detection , 2010, Pattern Recognit..

[10]  Xiangzhi Bai,et al.  Infrared image enhancement through contrast enhancement by using multiscale new top-hat transform , 2011 .

[11]  S. R. Kannan,et al.  Modified fuzzy c-means algorithm for segmentation of T1-T2-weighted brain MRI , 2011, J. Comput. Appl. Math..

[12]  Danilo P. Mandic,et al.  Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition , 2015, Sensors.

[13]  Vladimir Petrovic,et al.  Objective image fusion performance measure , 2000 .

[14]  Qi Li,et al.  Fusion of visible and infrared images using saliency analysis and detail preserving based image decomposition , 2013 .

[15]  Gonzalo Pajares,et al.  A wavelet-based image fusion tutorial , 2004, Pattern Recognit..

[16]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[17]  Xiangzhi Bai,et al.  Fusion of infrared and visual images through region extraction by using multi scale center-surround top-hat transform. , 2011, Optics express.

[18]  Xiangzhi Bai,et al.  Detail preserved fusion of infrared and visual images by using opening and closing based toggle operator , 2014 .

[19]  Veysel Aslantas,et al.  A comparison of criterion functions for fusion of multi-focus noisy images , 2009 .

[20]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[21]  Yun Zhang,et al.  Wavelet based image fusion techniques — An introduction, review and comparison , 2007 .

[22]  Shutao Li,et al.  Multifocus image fusion using region segmentation and spatial frequency , 2008, Image Vis. Comput..

[23]  J. Wesley Roberts,et al.  Assessment of image fusion procedures using entropy, image quality, and multispectral classification , 2008 .

[24]  Bhabatosh Chanda,et al.  Fusion of 2D grayscale images using multiscale morphology , 2001, Pattern Recognit..

[25]  Yu Zhang,et al.  Spatial information based FCM for infrared ship target segmentation , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[26]  Masha Maltz,et al.  A new multi-spectral feature level image fusion method for human interpretation , 2009 .

[27]  Nikolaos Mitianoudis,et al.  Pixel-based and region-based image fusion schemes using ICA bases , 2007, Inf. Fusion.

[28]  Luciano Alparone,et al.  Remote sensing image fusion using the curvelet transform , 2007, Inf. Fusion.

[29]  Shuaiqi Liu,et al.  Infrared and visible image fusion based on object extraction and adaptive pulse coupled neural network via non-subsampled Shearlet transform , 2014, 2014 12th International Conference on Signal Processing (ICSP).

[30]  Vassilia Karathanassi,et al.  Investigation of the Dual-Tree Complex and Shift-Invariant Discrete Wavelet Transforms on Quickbird Image Fusion , 2007, IEEE Geoscience and Remote Sensing Letters.

[31]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[32]  Xiangzhi Bai,et al.  Morphological infrared image enhancement based on multi-scale sequential toggle operator using opening and closing as primitives , 2015 .

[33]  Hyung-Sup Jung,et al.  Multi-Sensor Fusion of Landsat 8 Thermal Infrared (TIR) and Panchromatic (PAN) Images , 2014, Sensors.

[34]  Pan Lin,et al.  Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks , 2014, Sensors.

[35]  Xiangzhi Bai,et al.  Enhancement of microscopy mineral images through constructing alternating operators using opening and closing based toggle operator , 2014 .

[36]  Zhongliang Jing,et al.  Multi-focus image fusion using pulse coupled neural network , 2007, Pattern Recognit. Lett..

[37]  Xiangzhi Bai,et al.  Infrared dim small target enhancement using toggle contrast operator , 2012 .

[38]  X. BAI.  Morphological enhancement of microscopy mineral image using opening‐ and closing‐based toggle operator , 2014, Journal of microscopy.

[39]  Changming Sun,et al.  Splitting touching cells based on concave points and ellipse fitting , 2009, Pattern Recognit..

[40]  Stanley R Sternberg,et al.  Grayscale morphology , 1986 .

[41]  Xiangzhi Bai,et al.  Multiscale top-hat selection transform based infrared and visual image fusion with emphasis on extracting regions of interest , 2013 .

[42]  C. O'Hara,et al.  Concepts of Image Fusion in Remote Sensing Applications , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[43]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[44]  Henk J. A. M. Heijmans,et al.  A new quality metric for image fusion , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).