Infrared and visible image fusion via saliency analysis and local edge-preserving multi-scale decomposition.

To retain the details of a visible image with a discernible target area, we propose a multi-scale decomposition image fusion method based on a local edge-preserving (LEP) filter and saliency detection. We first use a LEP filter to decompose the infrared and visible images. Then, a modified saliency detection method is utilized to detect the salient target areas of an infrared image, which determine the base layer's weights of fusion strategy. Finally, each layer is reconstructed to obtain a visually pleasing fused image. Comparison with 11 other state-of-the-art methods reveals the superiority of the proposed method in terms of quality and quantity results.

[1]  Sun Li,et al.  Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters , 2016, Inf. Fusion.

[2]  Bo Gu,et al.  Local Edge-Preserving Multiscale Decomposition for High Dynamic Range Image Tone Mapping , 2013, IEEE Transactions on Image Processing.

[3]  Alexander Toet,et al.  Fusion of visible and thermal imagery improves situational awareness , 1997, Defense, Security, and Sensing.

[4]  M. Hossny,et al.  Comments on 'Information measure for performance of image fusion' , 2008 .

[5]  Sim Heng Ong,et al.  Remote Sensing Image Registration Using Multiple Image Features , 2017, Remote. Sens..

[6]  Shutao Li,et al.  Pixel-level image fusion: A survey of the state of the art , 2017, Inf. Fusion.

[7]  Jun Chen,et al.  Infrared and visible image fusion using total variation model , 2016, Neurocomputing.

[8]  Alan L. Yuille,et al.  Non-Rigid Point Set Registration by Preserving Global and Local Structures , 2016, IEEE Transactions on Image Processing.

[9]  Shutao Li,et al.  Image Fusion With Guided Filtering , 2013, IEEE Transactions on Image Processing.

[10]  Xiaohai He,et al.  Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter , 2015 .

[11]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[12]  Yu Liu,et al.  A general framework for image fusion based on multi-scale transform and sparse representation , 2015, Inf. Fusion.

[13]  Zhuowen Tu,et al.  Robust Point Matching via Vector Field Consensus , 2014, IEEE Transactions on Image Processing.

[14]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

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

[16]  Cedric Nishan Canagarajah,et al.  Pixel- and region-based image fusion with complex wavelets , 2007, Inf. Fusion.

[17]  Simon J. Godsill,et al.  A Nonreference Image Fusion Metric Based on the Regional Importance Measure , 2009, IEEE Journal of Selected Topics in Signal Processing.

[18]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[19]  Vps Naidu,et al.  Image Fusion Technique using Multi-resolution Singular Value Decomposition , 2011 .

[20]  Alexander Toet,et al.  Iterative guided image fusion , 2016, PeerJ Comput. Sci..

[21]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[22]  M. Bar Visual objects in context , 2004, Nature Reviews Neuroscience.

[23]  屈小波 Xiaobo Qu,et al.  Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain , 2008 .

[24]  Vassilis Tsagaris,et al.  Fusion of visible and infrared imagery for night color vision , 2005, Displays.

[25]  Zhongliang Jing,et al.  Evaluation of focus measures in multi-focus image fusion , 2007, Pattern Recognit. Lett..

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

[27]  Alexander A. Sawchuk,et al.  Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Alexander Toet,et al.  Image fusion by a ration of low-pass pyramid , 1989, Pattern Recognit. Lett..

[29]  Ling Shao,et al.  Visual Tracking Under Motion Blur , 2016, IEEE Transactions on Image Processing.

[30]  Yuanyuan Wang,et al.  Biological image fusion using a NSCT based variable-weight method , 2011, Inf. Fusion.

[31]  Lihi Zelnik-Manor,et al.  Context-Aware Saliency Detection , 2012, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Alan L. Yuille,et al.  Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples , 2016, IEEE Transactions on Image Processing.

[33]  Jiayi Ma,et al.  Infrared and visible image fusion via gradient transfer and total variation minimization , 2016, Inf. Fusion.

[34]  Yong Jiang,et al.  Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter , 2014, IET Image Process..

[35]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Zhuowen Tu,et al.  Regularized vector field learning with sparse approximation for mismatch removal , 2013, Pattern Recognit..

[37]  G. Meenakshi,et al.  STUDY OF MOLECULAR INTERACTION IN CERTAIN BINARY AND TERNARY LIQUID SYSTEM S BY EXPOSING IT TO ULTRASONIC FREQUENCY OF 2 MHz AT 293.15 K , 2015 .

[38]  Zhifeng Gao,et al.  Fusion of infrared and visible images for night-vision context enhancement. , 2016, Applied optics.

[39]  Wen-Rong Wu,et al.  Image Contrast Enhancement Based on a Histogram Transformation of Local Standard Deviation , 1998, IEEE Trans. Medical Imaging.

[40]  Ji Zhao,et al.  Non-rigid visible and infrared face registration via regularized Gaussian fields criterion , 2015, Pattern Recognit..