压缩感知在图像去噪和重构中的研究进展

Research on Compressed Sensing in Image Denoising and Reconstruction

压缩感知理论基于信号的稀疏性在远低于奈奎斯特采样频率下直接采集信号,可以有效地减少信号的采样时间和降低信号的存储及传输消耗。从信号的稀疏化、观测矩阵的设计和信号的重构3个方面阐述了CS理论框架;综述了CS在图像去噪领域和图像重构领域中的研究进展,展示了CS强大的图像去噪和图像重构能力;总结了CS在图像处理中存在的问题,并展望了CS在图像处理中的应用前景。

Compressed Sensing （CS） theory, based on the sparseness of signals, can be directly used to acquire signals at far low the traditional Nyquist frequency. It not only decreases the time of sampling efficiently, but also reduces the cost of the storage and transmission. The principles of CS are introduced as follow： 1 ） the sparseness of the signal, 2） the design of measurement matrix, 3） the reconstruction of the signal. The application and development of CS in image denoising and reconstruction are surveyed. The CS plays an important role in the image denoising and reconstruction. The problems existing in the image processing are pointed out and its application prospect is looked forward to.