Adaptive compressed sensing of remote-sensing imaging based on the sparsity prediction

Senlin Yang; Xilong Li; Xin Chong

doi:10.1117/12.2284248

24 October 2017 Adaptive compressed sensing of remote-sensing imaging based on the sparsity prediction

Senlin Yang, Xilong Li, Xin Chong

Proceedings Volume 10463, AOPC 2017: Space Optics and Earth Imaging and Space Navigation; 1046312 (2017) https://doi.org/10.1117/12.2284248
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China

Abstract

The conventional compressive sensing works based on the non-adaptive linear projections, and the parameter of its measurement times is usually set empirically. As a result, the quality of image reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was given. Then an estimation method for the sparsity of image was proposed based on the two dimensional discrete cosine transform (2D DCT). With an energy threshold given beforehand, the DCT coefficients were processed with both energy normalization and sorting in descending order, and the sparsity of the image can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of image effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparse degree estimated with the energy threshold provided, the proposed method can ensure the quality of image reconstruction.

Citation Download Citation

Senlin Yang, Xilong Li, and Xin Chong "Adaptive compressed sensing of remote-sensing imaging based on the sparsity prediction", Proc. SPIE 10463, AOPC 2017: Space Optics and Earth Imaging and Space Navigation, 1046312 (24 October 2017); https://doi.org/10.1117/12.2284248

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