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SUMMARYCompressive sampling (CS) offers bandwidth, power, and memory size reduction compared to conventional (Nyquist) sampling. These are very attractive features for the design of modern complementary metal-oxide semiconductor (CMOS) image sensors, cameras, and camera systems. However, very few integrated circuit (IC) designs based on CS exist because of the missing link between the well-established CS theory on one side, and the practical aspects/effects related to physical IC design on the other side. This paper focuses on the application of compressed image acquisition in CMOS image sensor integrated circuit design. A new CS scheme is proposed, which is suited for hardware implementation in CMOS IC design. All the main physical non-idealities are explained and carefully modeled. Their influences on the acquired image quality are analyzed in the general case and quantified for the case of the proposed CS scheme. The presented methodology can also be used for different CS schemes and as a general guideline in future CS based CMOS image sensor designs.

An edge-detection scheme suitable for machine vision and digital motion detection applications is presented. The scheme is inspired by the human visual system (human retina) and modified for a compact and scalable CMOS hardware implementation. In addition, pixel circuit and implementation of the scheme on a CMOS focal-plane are proposed and simulation results are presented. The circuit performs thresholding and single-bit quantization of high-frequency image content over multiple frequency bands using a low-complexity down-sampling scheme. As a result, a 1-bit per pixel output is obtained which results in efficient edge-detection of the image content. Depending on the number of the utilized frequency bands, the presented scheme achieves image compression levels that range from 1 BPP (bit-per-pixel) to 1.33 BPP. The proposed acquisition technique and the corresponding CMOS circuit can easily be adjusted to various imaging applications and scaled towards new CMOS technology nodes and high resolution image sensors.