Image fusion based on visual salient features and the cross-contrast

Low frequency subband coefficients are selected based on visual salient features.Bandpass directional subband coefficients are selected by the cross-contrast.Three maps of visual salient features are constructed based on visual saliency. To extract and combine the features of the original images, a novel algorithm based on visual salient features and the cross-contrast is proposed in this paper. Original images were decomposed into low frequency subband coefficients and bandpass direction subband coefficients by using the nonsubsampled contourlet transform. Three maps of visual salient features are constructed based on visual salient features the local energy, the contrast and the gradient respectively, and low-frequency subband coefficients are got by utilizing these visual saliency maps. The cross-contrast is obtained by computing the ratio between the local gray mean of bandpass direction subband coefficients and the local gray mean of fused low-frequency subband coefficients. Bandpass direction subband coefficients is goted by the cross-contrast. Comparison experiments have been performed on different image sets, and experimental results demonstrate that the proposed method performs better in both subjective and objective qualities.

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

[2]  Jun Wang,et al.  Image fusion with nonsubsampled contourlet transform and sparse representation , 2013, J. Electronic Imaging.

[3]  Mei Yang,et al.  A novel algorithm of image fusion using shearlets , 2011 .

[4]  Rosa Rodriguez-Sánchez,et al.  Axiomatic approach to computational attention , 2010, Pattern Recognit..

[5]  Shutao Li,et al.  Performance comparison of different multi-resolution transforms for image fusion , 2011, Inf. Fusion.

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

[7]  Yongdong Zhang,et al.  A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors , 2014, IEEE Signal Processing Letters.

[8]  Bruno Aiazzi,et al.  Multispectral fusion of multisensor image data by the generalized Laplacian pyramid , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[9]  Yi Chai,et al.  Multifocus image fusion scheme using focused region detection and multiresolution , 2011 .

[10]  M. Beaulieu,et al.  Multi- spectral image resolution refinement using stationary wavelet transform , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[11]  Padma Ganasala,et al.  Multimodality medical image fusion based on new features in NSST domain , 2014 .

[12]  Yingjie Yang,et al.  Irregularity-based image regions saliency identification and evaluation , 2014, Multimedia Tools and Applications.

[13]  Yongjian Hu,et al.  Image Fusion Based Visible Watermarking Using Dual-Tree Complex Wavelet Transform , 2003, IWDW.

[14]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

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

[16]  Zhenya Wu,et al.  Multi-focus image fusion based on the non-subsampled contourlet transform , 2012 .

[17]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..

[18]  Deepu Rajan,et al.  Random Walks on Graphs for Salient Object Detection in Images , 2010, IEEE Transactions on Image Processing.

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

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

[21]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[22]  Weisi Lin,et al.  Saliency Detection in the Compressed Domain for Adaptive Image Retargeting , 2012, IEEE Transactions on Image Processing.

[23]  Shutao Li,et al.  Multisensor Remote Sensing Image Fusion Using Stationary Wavelet Transform: Effects of Basis and Decomposition Level , 2008, Int. J. Wavelets Multiresolution Inf. Process..

[24]  Li Chen,et al.  Multi-focus image fusion using a bilateral gradient-based sharpness criterion , 2011 .

[25]  Myeong-Ryong Nam,et al.  Fusion of multispectral and panchromatic Satellite images using the curvelet transform , 2005, IEEE Geoscience and Remote Sensing Letters.

[26]  Andrew K. C. Wong,et al.  A gray-level threshold selection method based on maximum entropy principle , 1989, IEEE Trans. Syst. Man Cybern..

[27]  Rafael García,et al.  Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Gang Xiao,et al.  Feature-based image fusion scheme for satellite recognition , 2010, 2010 13th International Conference on Information Fusion.

[29]  Yongdong Zhang,et al.  Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[30]  Weisi Lin,et al.  Saliency-based image retargeting in the compressed domain , 2011, ACM Multimedia.

[31]  Alexander Toet,et al.  Merging thermal and visual images by a contrast pyramid , 1989 .

[32]  Yu Liu,et al.  Multi-focus image fusion with dense SIFT , 2015, Inf. Fusion.

[33]  Zheng Liu,et al.  A novel image fusion framework for night-vision navigation and surveillance , 2015, Signal Image Video Process..

[34]  Gao Guorong,et al.  Multi-focus image fusion based on non-subsampled shearlet transform , 2013, IET Image Process..

[35]  Xiangzhi Bai.  Image fusion through feature extraction by using sequentially combined toggle and top-hat based contrast operator. , 2012, Applied optics.

[36]  Qiang Zhang,et al.  Multifocus image fusion using the nonsubsampled contourlet transform , 2009, Signal Process..

[37]  Minh N. Do,et al.  The Nonsubsampled Contourlet Transform: Theory, Design, and Applications , 2006, IEEE Transactions on Image Processing.

[38]  Gemma Piella,et al.  A general framework for multiresolution image fusion: from pixels to regions , 2003, Inf. Fusion.

[39]  J. Dobrowolski,et al.  Transparent, conducting indium tin oxide films formed on low or medium temperature substrates by ion-assisted deposition. , 1987, Applied optics.

[40]  Bin Xiao,et al.  Union Laplacian pyramid with multiple features for medical image fusion , 2016, Neurocomputing.

[41]  Yi Chai,et al.  Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain , 2010 .

[42]  Yong Jiang,et al.  Image fusion with morphological component analysis , 2014, Inf. Fusion.

[43]  Tian Pu,et al.  Contrast-based image fusion using the discrete wavelet transform , 2000 .

[44]  Yang Yi,et al.  Key frame extraction based on visual attention model , 2012, J. Vis. Commun. Image Represent..

[45]  Anil K. Jain,et al.  Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images , 1994, IEEE Trans. Geosci. Remote. Sens..

[46]  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 .

[47]  U. Hoelscher-Hoebing,et al.  Unsupervised image segmentation and image fusion for multi-beam/multi-aspect sidescan sonar images , 1998, IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259).

[48]  Vladimir S. Petrovic,et al.  Gradient-based multiresolution image fusion , 2004, IEEE Transactions on Image Processing.

[49]  Tao Chen,et al.  Remote sensing image fusion based on ridgelet transform , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..