A New Method for Compressed Sensing Color Images Reconstruction Based on Total Variation Model

A new method based on the total variation model, applicable to reconstruct the compressed sensing color images, is proposed. At first, the compressed sensing color images should be converted form the RGB color space to the CMYK space, and the compressed sensing color images in the CMYK space can match exactly with the quaternion matrix. Next, the amplitude and the different four phase information of the quaternion matrix is treated as the smoothing constraints for the compressed sensing problem in order to reconstruct the color images more effectively. Finally, the gradient projection method is used to solve the compressed sensing problem. Experimental results show that this new method can reconstruct color images better than some traditional methods.

[1]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[2]  Hongyang Chao,et al.  High-quality image restoration from partial mixed adaptive-random measurements , 2013, Multimedia Tools and Applications.

[3]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[4]  Lu Wang,et al.  Structured sparsity-driven autofocus algorithm for high-resolution radar imagery , 2016, Signal Process..

[5]  Rabab K. Ward,et al.  Compressed sensing of color images , 2010, Signal Process..

[6]  Ning Sun,et al.  Colour compressed sensing imaging via sparse difference and fractal minimisation recovery , 2015, IET Image Process..

[7]  Jianwei Ma,et al.  Compressed sensing of complex-valued data , 2012, Signal Process..

[8]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[9]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[10]  Yoram Bresler,et al.  Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing , 2015, IEEE Transactions on Computational Imaging.

[11]  Qi Dai,et al.  The Physics of Compressive Sensing and the Gradient-Based Recovery Algorithms , 2009, ArXiv.

[12]  Ke Lu,et al.  Compressed sensing based remote sensing image reconstruction via employing similarities of reference images , 2016, Multimedia Tools and Applications.

[13]  Ou Xie,et al.  Compression and encryption for remote sensing image using chaotic system , 2015, Secur. Commun. Networks.

[14]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[15]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[16]  Alfred Mertins,et al.  Super resolution reconstruction method for time-of-flight range data using complex compressive sensing , 2011, 2011 IEEE International Instrumentation and Measurement Technology Conference.