CMOS (complementary metal-oxide-semiconductor transistor) imaging measured value obtaining system based on compressed sensing and method thereof

Abstract

The invention discloses a CMOS (complementary metal-oxide-semiconductor transistor) imaging measured value obtaining system based on compressed sensing and a method thereof. The system provides an analogue pixel matrix to the system by a CMOS image sensor and is characterized by comprising a linear feedback displacement register, a displacement register, a line selector, a multi-path selector and an analogue/digital converter. Compared with the prior art, the system has the characteristics of generality, encryption, robustness, scalability and the like of the imaging system based on compressed sensing, can shorten the measured value obtaining time and effectively reduce the power consumption of the sensor in comparison with a CMOS imaging system based on random convolution, and has simple structure and is easy to implement in comparison with other imaging systems based on compressed sensing; moreover, the system effectively shortens the measured value obtaining time on the basis of a parallel processing idea, and remarkably reduces the power consumption of the CMOS image sensor.

Description

technical field [0001] The invention relates to the technical field of image signal processing, in particular to a CMOS imaging measurement value acquisition system and method in the compressed imaging field. Background technique [0002] Compressive sensing (compressive sensing, CS) is a new research field proposed in recent years. It is based on the sparse representation and approximation theory of signals, and makes full use of the sparse characteristics of signal structures. Uncorrelated measurements of data enable perception of high-dimensional sparse signals. It breaks through the limitations of the Nyquist sampling theorem, and simultaneously performs signal sampling and compression, making low-sampling and high-resolution signal reconstruction possible. Compressive sensing theory has brought about a revolution in signal acquisition theory, and has broad application prospects in the fields of compressed imaging, analog information conversion, medical image processing...

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Problems solved by technology

Compared with the method proposed by R. Robucci et al., this method has the advantages of simple system implementation, good anti-noise performance and nonlinearity, but it uses a shift register to generate a pseudo-random code, and places a bit in each pixel storage unit, which requires an additional process flow, and this method needs to perform more shift operations when acquiring the measured value, thereby reducing the image acquisition efficiency and power consumption

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