A Primary Research On Image Reconstruction And Denoising Based On Compressed Sensing

In traditional information acquisition, the sampling rate is decided according to the Nyquist Criteria, i.e., the sampling rate must be twice the bandwidth of the signal at least. However, the huge information amount caused that signal’s bandwidth and the sampling rate is getting bigger and bigger. The hardware and complicated calculation are problems which obstacle the information technology’s development. In 2006, Donoho and Candes proposed a novel theory-Compressed Sensing (Compressive Sampling, CS). This theory is different from Nyquist criteria, according to it, just as some conditions satisfied, the signal can be recovered from very few measurements, i.e., it realized low rate sampling. This paper focuses on signal reconstruction and denoising based on Compressed Sensing, A research on MR imaging system based on Compressed sensing which could improve the imaging speed and remove noise efficiently. The basic command of compressed sensing denoising is signal’s sparsity, this paper realize multiscale compressed sensing denoising based on contourlet.The main works as follow:1. A research on Compressed Sensing theory, signal reconstruction and denoising based on compresses sensing.2. A deep research on MR Imaging principles, and the properties which fit into Compressed Sensing. In traditional MR Imaging system, the imaging speed is very low because of Nyquist criteria and the hardware and patients influences. Fortunately, MR images fits into Compressed Sensing. Compressed Sensing MR Image reconstruction is realized in this paper, which improves imaging speed a lot.3. In data acquisition, noise is inevitable acquired at the same time, so the MR image quality is infected. A research on traditional image denoising and image denoising algorithm based on Compressed Sensing. Because of the sparsity of MR images in spatial domain and the wavelet transform domain, Compressed Sensing MR image denoising based on identity transform and wavelet transform are realized, and then a combined sparse transform, which is made up of the two sparse transforms, is adopted to realize Compressed Sensing image denoising in this paper. The algorithm of Compressed Sensing MR image denoising outperforms traditional denoising algorithms.4. The Contourlet transform outperforms wavelet transform both in the sparsity and edge preserving in image representation. Based on analysis of Contourlet transform coefficients distribution and compressed sensing denoising theory, this paper combine Contourlet transform with compressed sensing to realize multiscale Compressed Sensing image denoising based on contourlet which outperforms traditional Contourlet denoising algorithm.