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Recently there has emerged a lot of work that integrates the compressive sampling theory into watermarking schemes. The subtle changes in local space domain of the signal can lead to distinct disturbance in all the measurements. It will lead to the poor robustness when the watermark is directly embedded into the compressive sampling measurements. To solve this problem, a robust digital watermarking...
Audio information hiding is a technique, which is used to hide secret message into the audio flows. In this paper, an audio information hiding scheme with high-capacity is proposed, which uses both Compressed Sensing (CS) and psychoacoustic model algorithms for improving the capacity of hidden message and the auditory imperceptibility. The scheme first applies CS technology to compress the secret...
The existing compressed sensing image reconstruction algorithms cannot combine reconstruction effect with reconstruction speed at the same time. A new image reconstruction algorithm is proposed for compressed sensing. The new algorithm retains the advantages of block-sampling compressed sensing by the image blocks segmentation. The subblocks of the image edges are extracted and then the edges structure...
Compressed sensing is a topic that has recently gained much attention in high-speed underwater acoustic (UWA) communications. Combined the MIMO communication system with OFDM technology, they can realize high-speed data communication over UWA channels and overcome the fading problem efficiently. In this paper performance analysis of compressed sensing (CS) channel estimation algorithms for underwater...
This paper introduces the fundamental knowledge of compressed sensing theory, and analyzes the important reconstruction algorithms such as orthogonal matching pursuit, subspace pursuit, but we should know the sparse degree. The sparsity adaptive matching pursuit algorithm can be terminated by setting the conditions to make adaptive sparse degree. This paper puts forward a modified sparsity adaptive...
In recent years, with the theory of compressed sensing being proposed and applied widely, the sparse representation method has become one of the hotspots to handle the superresolution problem. Usually, this kind of algorithms use only one dictionary pair for all low-resolution patches, which makes the recovered results less satisfied due to its bad adaptability. To overcome such problem, in this paper,...
This paper proposes a greedy reconstruction algorithm to recover sparse signal from compressed measurements, called multipath subspace pursuit (MSP). Different from the subspace pursuit (SP), the MSP creates several candidates of the support set when iteration ends. We can select one best candidate as the final support set. At the beginning of each iteration, MSP uses a nuanced method to add the atoms...
Compressed sensing based on sparsity has been concerned in the field of signal reconstruction. Meanwhile nonsubsampled contourlet transform, proposed as a development to contourlet, not only provides flexible multi-scale, multi-direction sparse image decomposition, but also features with shift-invariance property which is beneficial to image denoising. This paper combines threshold operatoion in nonsubsampled...
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