Image fusion methods based on compressed sensing: theory and application

Image fusion can provide a more comprehensive scene description. Applying the technology of compressed sensing to image fusion can reduce the amount of data and accelerate the algorithm processing, while sprase representation,compression sampling and signal reconstruction are the key to the application of compressed sensing in image fusion. Researching on the above problems will be benificial to reduce the time-consuming of fusion process. both theory and application are analyzied and summarized for make compressed sensing better applied to image fusion, thus providing valuable guidance for better solution to be proposed.

[1]  吉桐伯 Ji Tong-bo,et al.  Fusion of infrared and visible images based on target segmentation and compressed sensing , 2016 .

[2]  尹雯 Yin Wen,et al.  Remote Sensing Image Fusion Based on Sparse Representation , 2013 .

[3]  Kai Kang,et al.  An infrared and visible image fusion algorithm based on MAP , 2019, Other Conferences.

[4]  Hassan Ghassemian,et al.  Multimodal image fusion via sparse representation and clustering-based dictionary learning algorithm in NonSubsampled Contourlet domain , 2016, 2016 8th International Symposium on Telecommunications (IST).

[5]  X. Li,et al.  Efficient fusion for infrared and visible images based on compressive sensing principle , 2011 .

[6]  Cao Li.  Remote sensing image fusion algorithm based on DCT , 2015 .

[7]  Xin Zhou,et al.  Image Fusion Based on Compressed Sensing , 2014 .

[8]  Zeng Jun-guo.  Image super resolution based on couple dictionary learning , 2013 .

[9]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

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

[11]  Wei-Ping Zhu,et al.  Fusion framework for multi-focus images based on compressed sensing , 2013, IET Image Process..

[12]  Hui Liu.  Image Fusion Method based on Non-Subsampled Contourlet Transform , 2012, J. Softw..

[13]  Xu Wei,et al.  Fusion of Remote Sensing Image with Compressed Sensing Based on Wavelet Sparse Basis , 2014, 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation.

[14]  Cedric Nishan Canagarajah,et al.  Compressive image fusion , 2008, 2008 15th IEEE International Conference on Image Processing.

[15]  Qionghai Dai,et al.  Classification-based image-fusion framework for compressive imaging , 2010, J. Electronic Imaging.

[16]  Yi Chai,et al.  A novel dictionary learning approach for multi-modality medical image fusion , 2016, Neurocomputing.