A Compressive Sensing Imaging System Based On CMOS Image Sensor

The theory of compressive sensing proposed in recent years is different from the traditional sampling theory-Nyquist sampling theorem. Compressive sensing theory indicates that if the signal is sparse in a transform domain, then we can make a transformed high-dimensional signal to project onto a low-dimensional space using a measurement matrix which is not related to the transform base. The original signal can be reconstructed with high probability from these small measurements by solving a optimization problemTraditional CMOS image sensor obtains the pixel array by analog readout circuit, and then converts them to digital signals before Nyquist-sampling. A lot of redundant data is discarded before compression. Taking the compressive sensing technology broach to the CMOS image sensors, it can use compressive sensing on the analog signal directly after the photoelectric conversion. Compressive sensing can acquire and compress the signal at the same time, so the data passing through the A/D converter can be reduced. This can improve the speed of A/D conversion and cut down power consumption.In view of the existing CMOS image sensor systems which based on image compressive sensing algorithm change the structure of the sensor circuit a lot, and are difficult implemented in hardware because of too much additional circuit. This thesis presents a parallel processing strategy for analog pixel matrix of CMOS image sensor. It can reduce the burden of A/D converter module, also can reduce the power consumption of CMOS image sensors. The implementation of the circuit is simple, and it does not need more additional circuit. Simulation results show that the proposed structure can get the measurements quickly and effectively. The subjective and objective quality of the reconstructed image has little loss.Measurement matrix is an important part of the front-end of the imaging system, Toeplitz-structure measurement matrix generates and stores very easily. Applications in the imaging system can reduce the complexity and power consumption of the image system. Reconstruction algorithms is the main object of study at the back-end of the imaging system. Based on analysis of the existing algorithms and considering the structure of the parallel processing architecture, a improved algorithm has been put forward.