A Single Pixel Camera Based on White-noise Compressed Sensing
Dharmpal TakharJason N. LaskaDror BaronMichael B. WakinMarco F. DuarteShriram SarvothamRichard G. BaraniukKevin F. Kelly
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We design a camera by combining a micromirror-array with a single optical sensor and exploiting compressed sensing based on projections with white-noise basis. A practical image/video camera is developed based on this concept and realized.To widen measuring range and suppress background light, a CMOS time-of-flight image sensor with high dynamic range digital pixel is proposed. The sensing charge is quantized by extended-counting analogue-to-digital converter (ADC) which consists of pixel coarse quantization and column fine quantization. To maximize dynamic range in limited pixel area, the coarse quantization circuit is shared by 2×2 pixels, in which a novel up/down counter with a small number of transistors is proposed. Based on the pixel circuit, a 32×32 prototype TOF imager with 12μm-pitch digital pixel is designed in 0.11μm 1P4M CMOS image sensor technology. Simulation results show that a 104.6dB dynamic range of this ADC for wide measuring range is achieved at a frame rate of 50Hz.
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Angle-selective pixels fabricated by using metal layers of the image sensor chip are proposed. Four incident-angle-selective pixels share one aperture. The selective pixel has a peak at 40° and full width at half maximum of ∼23°. An image sensor composed of normal and angle-sensitive pixels was designed and fabricated. The reconstruction of a high-resolution image from the blurred image using five different views is demonstrated.
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A 256/spl times/192 pixel FEA image sensor was fabricated and tested. Experimental results showed that the sensor had a sufficient dynamic range and proper resolution for its small pixel size, demonstrating its feasibility as a practical image sensor with many small pixels.
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Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as super-resolution of images, the sensing matrix has a non-random structure with highly correlated columns. Here we present a compressive sensing recovery algorithm that exploits this correlation structure. We provide algorithmic justification as well as empirical comparisons.
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Compressed Sensing is a new sampling theorem,it points out that if a signal can be compressed under some conditions,that a very accurate reconstruction can be obtained from a relatively small number of non-traditional samples.On the basis of compressed sensing,the paper presents multiscale compressed sensing.The numerical experiments demonstrate that multiscale compressed sensing can give better quality reconstruction than a literal deployment of the compressed sensing methodology.
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Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as super-resolution of images, the sensing matrix has a non-random structure with highly correlated columns. Here we present a compressive sensing recovery algorithm that exploits this correlation structure. We provide algorithmic justification as well as empirical comparisons.
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This paper presents a novel approach, compressive mobile sensing, to use mobile sensors to sample and reconstruct sensing fields based on compressive sensing. Compressive sensing is an emerging research field based on the fact that a small number of linear measurements can recover a sparse signal without losing any useful information. Using compressive sensing, the signal can be recovered by a sampling rate that is much lower than the requirements from the well-known Shannon sampling theory. The proposed compressive mobile sensing approach has not only the merits of compressive sensing, but also the flexibility of different sampling densities for areas of different interests. A special measurement process makes it different from normal compressive sensing. Adopting importance sampling, compressive mobile sensing enables mobile sensors to move adaptively and acquire more samples from more important areas. A motion planning algorithm is designed based on the result of sparsity analysis to locate areas of more interests. At last, experimental results of 2-D mapping are presented as an implementation compressive mobile sensing.
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The employment of small pixel size is very crucial to the physical implementation of very high resolution CMOS APS image sensors. This is because of the restriction imposed by the required lithography method utilized in modern sub-micron CMOS fabrication processes on the size of the image sensor chip. Furthermore, the cost of a CMOS active pixel sensor (APS) image sensor chip substantially goes down as the pixel size goes down, whether the image sensor chip is very high resolution or not. However, there exist several optical limitations on the pixel size that make it imprudent to further reduce the pixel size beyond those limitations. A major limitation on the reduction of pixel size is that pixel sensitivity is significantly reduced as pixel size goes down. One may take refuge in using low f-number optics to allow more photons into the pixel to compensate for the reduced sensitivity of the small pixel. However, the employment of low f-number optics is associated with high cost, which may offset the low cost advantage of image sensor chips that have small pixels. Additionally, low f-number optics may introduce undesirable aberrations. Another major limitation on the reduction of pixel size is that cross-talk is considerably increased as the pixel size is decreased. Keeping cross-talk low is very critical to the proper operation of an image sensor, particularly color image sensors.
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Compressed sensing theory provides a new approach to acquire data as a sampling technique and makes sure that an original sparse signal can be reconstructed from few measurements. The construction of compressed sensing matrices is a central problem in compressed sensing theory. In this paper, the deterministic compressed sensing matrices with characters of finite fields are constructed and the coherence of the matrices are computed. Furthermore, the maximum sparsity of recovering the original sparse signals by using our compressed sensing matrices is obtained. Meanwhile, a comparison is made with the compressed sensing matrices constructed by DeVore based on polynomials over finite fields. In the numerical simulations, our compressed sensing matrix outperforms DeVore’s matrix in the process of recovering original sparse signals.
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This paper presents a 1/1.33-inch 108Mp (megapixel) CMOS image sensor (CIS) for mobile applications. It is a 3D-stacked CIS designed with 0. 8um unit pixels having dual conversion gain (DCG). This work in which the NONACELL technology is applied proves large-pixel effect which provides improved performance in low lux condition at submicron pixels. The number of photodiodes increases due to the NONACELL technology, the full well capacity (FWC) can be increased due to the proposed DCG technology as well. In addition, in the proposed high dynamic range (HDR) structure, image can be displayed with a frame rate that is 3 times higher than traditional 3D-HDR.
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