一种高重构质量低复杂度的高光谱图像压缩感知

doi:10.3969/j.issn.1001-2400.2011.03.007

Abstract

Abstract:

A new compression algorithm for hyperspectral images based on compressed sensing is proposed which has the advantages of high reconstruction quality and low complexity by exploiting the strong spectral correlations. At the encoder, the prediction parameter between the neighboring bands is first estimated using the prediction algorithm and transmitted to the decoder. The random measurements of each band are then made, quantized and transmitted to the decoder independently. At the decoder, a new reconstruction algorithm with the proposed initialization and stopping criterion is applied to reconstruct the current band with the assistance of its prediction band, which is derived from the previous reconstructed neighboring band and the received prediction parameter using the prediction algorithm. Experimental results show that the proposed algorithm not only obtains a gain of about 1.2dB but also greatly decreases decoding complexity. In addition, our algorithm has the characteristics of low-complexity encoding and easy hardware implementation.

Key words: hyperspectral imagery, compressed sensing, linear prediction, optimization

LIU Haiying；LI Yunsong；WU Chengke；Lü Pei. Compressed hyperspectral image sensing based on interband prediction[J].J4, 2011, 38(3): 37-41+120.

References

［1］ Miekainen J, Toivanen P. Clustered DPCM for the Lossless Compression of Hyperspectral Images［J］. IEEE Trans on Geosci Remote Sensing, 2003, 41(12): 2943-2946.
［2］ Zhang J, Liu G Z. A Novel Lossless Compression for Hyperspectral Images by Context-Based Adaptive Classified Arithmetic Coding in Wavelet Domain［J］. IEEE Geosci Remote Sensing Letters, 2007, 4(3): 461-465.
［3］ Ryan M J, Arnold J F. The Lossless Compression of AVIRIS Images by Vector Quantization［J］. IEEE Trans on Geosci Remote Sensing, 1997, 35(3): 546-550.
［4］ Candes E J. Compressive Sampling［C/OL］. ［2009-06-20］. http://www-stat.stanford.edy/candes/papers/CompressiveSampling.pdf.
［5］ Candes E J, Romberg J, Tao T. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information ［J］. IEEE Trans on Information Theory, 2006, 52(2): 489-509.
［6］ Donoho D L. Compressed Sensing［J］. IEEE Trans on Information Theory, 2006, 52(4): 1289-1306.
［7］ Romberg J. Imaging via Compressive Sampling［J］. IEEE Signal Processing Magazine, 2008, 25(2): 14-20.
［8］ Candes E J, Tao T. Near Optical Signal Recovery from Random Projections: Universal Encoding［J］. IEEE Trans on Information Theory, 2006, 52(12):5406-5425.
［9］刘丹华, 石光明, 周佳社, 等. 基于Compressed Sensing框架的图像多描述编码方法［J］. 红外与毫米波学报, 2009, 28(4): 298-302.
Liu Danhua, Shi Guangming, Zhou Jiashe, et al. New Method of Multiple Description Coding for Image Based on Compressed Sensing［J］. J Infrared Millim Waves, 2009, 28(4): 298-302.
［10］ Gan L, Do T T, Tran T D. Fast Compressive Imaging Using Scrambled Block Hadamard Ensemble［C/OL］. ［2009-08-16］. http://dsp.rice.edu/sites/dsp.rice.edu/files/cs/srambled_blk_WHT.pdf.
［11］ Figueiredo M A T, Nowak R D, Wright S J. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems ［J］. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597.
［12］ Brites C, Ascenso J, Pereira F. Studying Temporal Correlation Noise Modeling for Pixel Based Wyner-ziv Video Coding［C］//IEEE International Conference of Image Processing. Atlanta: IEEE, 2006: 273-276.

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Compressed hyperspectral image sensing based on interband prediction

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