A CMOS image sensor with analog pre-processing capability suitable for smart camera applications

Soell, Christopher; Shi, Lan; Baenisch, Andreas; Ussmueller, Thomas; Weigel, Robert

doi:10.1109/ispacs.2015.7432780

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…During the system level simulation of proposed filter, its comparison with the Mean filter performance was presented. But comparing it to the existing hardware mean filter designs, the power consumption of existing architecture presented in [12] is less, which is about of 55.3uA current consumption. While proposed method is based on more complex algorithm inspired from the biological neuron functionality, incorporating several computing blocks to the design which causes more power dissipation.…”

Section: Discussionmentioning

confidence: 99%

Neuromorphic Adaptive Edge-Preserving Denoising Filter

Irmanova

Krestinskaya

James

2017

2017 IEEE International Conference on Rebooting Computing (ICRC)

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In this paper, we present on-sensor neuromorphic vision hardware implementation of denoising spatial filter. The mean or median spatial filters with fixed window shape are known for its denoising ability, however, have the drawback of blurring the object edges. The effect of blurring increases with an increase in window size. To preserve the edge information, we propose an adaptive spatial filter that uses neuron's ability to detect similar pixels and calculates the mean. The analog input differences of neighborhood pixels are converted to the chain of pulses with voltage controlled oscillator and applied as neuron input. When the input pulses charge the neuron to equal or greater level than its threshold, the neuron will fire, and pixels are identified as similar. The sequence of the neuron's responses for pixels is stored in the serial-in-parallel-out shift register. The outputs of shift registers are used as input to the selector switches of an averaging circuit making this an adaptive mean operation resulting in an edge preserving mean filter. System level simulation of the hardware is conducted using 150 images from Caltech database with added Gaussian noise to test the robustness of edge-preserving and denoising ability of the proposed filter. Threshold values of the hardware neuron were adjusted so that the proposed edge-preserving spatial filter achieves optimal performance in terms of PSNR and MSE, and these results outperforms that of the conventional mean and median filters.

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Section: Discussionmentioning

confidence: 99%

Neuromorphic Adaptive Edge-Preserving Denoising Filter

Irmanova

Krestinskaya

James

2017

2017 IEEE International Conference on Rebooting Computing (ICRC)

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“…Linear analog circuits are a vital part of the integrated circuits and their accurate design and analysis is of utmost importance. For example, they are a fundamental component of a Complementary Metaloxidesemiconductor (CMOS) image sensor based digital camera, which provides significant advantages in comparison to the traditional Charge-coupled Device (CCD) imaging systems [1], [2]. Figure 1 presents a typical image sensor based signal processing architecture that is used for imaging and mixed-signal processing.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the quality of the image by reducing the random image noise, some analog preprocessing algorithms, composed of linear analog circuits (spatial filters), are applied. The resultant analog signal is converted to a digital signal using an A/D converter, which is then used for further digital image processing [1], [2]. Being a major part of a image processing architecture, there is a dire need of accurate design and analysis of linear analog circuits.…”

Section: Introductionmentioning

confidence: 99%

FASiM: A Framework for Automatic Formal Analysis of Simulink Models of Linear Analog Circuits

Rashid¹,

Gauhar²,

Hasan³

2020

Preprint

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Simulink is a graphical environment that is widely adapted for the modeling and the Laplace transform based analysis of linear analog circuits used in signal processing architectures. However, due to the involvement of the numerical algorithms of MATLAB in the analysis process, the analysis results cannot be termed as complete and accurate. Higherorder-logic theorem proving is a formal verification method that has been recently proposed to overcome these limitations for the modeling and the Laplace transform based analysis of linear analog circuits. However, the formal modeling of a system is not a straightforward task due to the lack of formal methods background for engineers working in the industry. Moreover, due to the undecidable nature of higher-order logic, the analysis generally requires a significant amount of user guidance in the manual proof process. In order to facilitate industrial engineers to formally analyze the linear analog circuits based on the Laplace transform, we propose a framework, FASiM, which allows automatically conducting the formal analysis of the Simulink models of linear analog circuits using the HOL Light theorem prover. For illustration, we use FASiM to formally analyze Simulink models of some commonly used linear analog filters, such as Sallen-key filters.

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