FUSIÓN MORFOLÓGICA DE IMÁGENES IR Y VISUALES UTILIZANDO EL MODELO LIP

Oscar Ricardo Delfín Santiesteban, Iván Ramón Teról Villalobos

Resumen


Resumen

La fusión de imágenes es el proceso de combinar la información de una escena que proviene de dos o más imágenes fuente en una sola con una mejor percepción visual y espacial que puede proporcionar detalles que en su conjunto, no pueden ser observados en las imágenes por separado. En este estudio, se presenta una metodología que permite realizar este procedimiento combinando el modelo de procesamiento logarítmico de imágenes (LIP Model) y las transformaciones morfológicas por reconstrucciones.

Palabras Claves: Imagen Visual, imagen IR, modelo LIP, morfología matemática.

 

MORPHOLOGICAL FUSION OF IR AND VISUAL IMAGES USING THE LIP MODEL


Abstract

Image fusion is the process of combining information from a scene that comes from two or more source images into a single one with better visual and spatial perception that can provide details that as a whole cannot be seen in separate images. In this study, a methodology is presented that allows performing this procedure combining the logarithmic image processing model (LIP Model) and the morphological transformations by reconstructions.

Keywords: LIP Model, IR Image, mathematical morphology, visual Image.


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Referencias


Gyaourova A., G. Bebis, I., Fusion of infrared and visible images for face recognition. Computer Vision–ECCV, 2004.

Lewis J. J., R.J. O’Callaghan, S.G. Nikolov, D.R. Bull. Pixel–and region-based image fusion with complex wavelets. Information Fusion, Volume 8, Issue 2, pp. 119–130, 2007.

Li S., X. Kang, J. Hu, B. Yang. Image matting for fusion of multifocus in dynamic scenes. Information Fusion 14, pp. 147–162, 2013.

Li T., Y. Wang, C. Chang, N. Hu, Y. Zheng, Color-Appearance based fusion of gray and pseudo-color images for medical applications. Information Fusion 19, pp. 103–114, 2014.

Ma J., C. Chen, C. Li, J. Huang, Infrared visible image fusion via gradient transfer and total variation minimization. Information Fusion 31, 2016.

Matsopoulos G. K., S. Marshall, Application of morphological pyramids: fusion of MR and CT phantoms. Journal of Visual Communications and Image Representation 6 (2), PP. 167 –207, 1995.

Mayet F., J.C. Pinoli, M. Jourlin, Physical Justification and applications of the LIP Model for the Processing of transmitted ligth images. Traitement du Signal, Volume 13 No. 3, 1996.

Michoud P., J.C. Pinoli, M. Jourlin, Les applications insdustrielle et biomedicales du módele LIP, Seizéme Colloque Gretsi, pp. 15–19, Septembre 1997.

Mukhopadhyay S., B. Chanda, Fusion of 2D grayscale images using multiscale morphology. Pattern Recognition 34, pp. 1939–1949, 2001.

Omar A., T. Stathaki. Image Fusion: an overview. Fifth International Conference on Intelligent System, modelling and Simulation, 2014.

Piella G., A general framework for multiresolution image fusion: from pixels to regions. Information Fusion 4, pp. 259-280, 2003.

Pinoli J. C., The Logarithmic Image Processing Model: Connection with Human Brihtness Perception and COnstrast Estimator. Journal of Mathematical Imaging and Vision 7, pp. 341–158, 1997.

Pohl C., J.L. Genderen, Multisensor image fusion in remote sensing. International Journal of Remote Sensing. 19 (5), pp. 823–854, 1998.

Singh R., A. Khare, Fusion of multimodal medical using Daubechies complex wavelet transform–A multiresoution approach. Information Fusion 19, pp. 49–60, 2014.

Toet A., Image Fusion by ratio of low–pass pyramind. Pattern Recognition Letters 9 (4), pp. 245–253, 1989.

Toet A., Hierarchical Image Fusion, Machine Vision and Applications, 1990.

Toet A., Multiscale contrast enhancement with applications to image fusion. Optical Engineering 31(5), pp. 1026-1031, may 1992.

Toet A., Merging thermal and visual images by a constrast pyramids. Optical Engineering 28(7), pp. 789 -792, July 1989.

Xue Z., R.S.Blum, Concealed Weapon Detection Using Color Image Fusion. Electrical and Computer Engineering Deparment. Lehigh University, 2003.

Yang S., M. Wang, L. Jiao. Fusion of multiespectral and panchromatic images based on support value transform and adaptive principal component analysis. Information Fusion 13, pp. 177–184, 2012.






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