An Optical Imaging System Based On CS Theory

Classic signal sampling to seek the best signal fidelity to be sampled, the sampling process must follow the widely recognized Shannon sampling theorem, the sampling frequency must be sampled more than twice the highest frequency signal in order to ensure full value from the sample reconstruct the original signal.The common approach in digital imaging today is to capture as many pixels as possible and later to compress the captured image by digital means. The compression is required for storage and communication purposes. At the end of these two stages, the optical capturing and digital compressing, the image is represented by much less numbers than the number of pixel captured. Fortunately, in recent years, new CS theory to make the data sampling technique, breaking the limitations of Shannon sampling theorem, CS theory on the sparse feature of the signal (image) reconstruction with non-adaptive, nonlinear good features, it has been wide attention.We review the basic concepts behind CS and describe the compressed imaging system. In this work we further elaborated the concept and results with the recently CI system. This implies, for example, that more object pixels may be reconstructed and visualized than the number of pixels of the image sensor.The achievements of paper are as follows:CS theory has mainly three parts:sparse signal transformation, non-correlation measurement, reconstruction algorithm.The basic idea behind CS is that an image can be accurately reconstructed from fewer measurements than the nominal number of pixels if it is compressible by a known transform such as Wavelet or Fourier. Through a random phase measurement matrix, a high-dimensional sparse signal will be projected into the low-dimensional space, the sparse sampling the original signal, through the non-linear reconstruction algorithm (StOMP), it can achieve a high probability of accurately reconstruct the original signal.One practical suggestion of the CS theory is that, given some technical condition, a compressed image of an object can be obtained by capturing random projections of object. Then, the image can be reconstructed by applying non-linear numerical reconstruction algorithms.Compressed imaging system (CI) mainly is composed of two parts:optical system and digital signal processing systems. For objects that have sparse representation in some known domain (e.g. Fourier or wavelet) the novel imaging systems has larger effective space-bandwidth-product than conventional imaging systems. A compressed imaging (CI) system is proposed that uses a digital mirror array device to randomly project the image on a single sensor. The CI system randomly projects the object field in the image plane with the help of random phase mask. The random phase mask can be viewed as a random scrambler of rays. The compressed image is captured with a single exposure. We also used a more advanced restoration algorithm. Simulations have shown that for synthetic images, exact reconstructions can be obtained from compressed images that have less pixels than the original image. By simulation, it has been verified the feasibility of the Compressed imaging system.