Ultralow-Power Processing Array for Image Enhancement and Edge Detection

Abstract

Abstract-This paper presents a massively parallel processing array designed for the 0.13µm 1.5V standard CMOS base process of a commercial 3-D TSV stack. The array, which will constitute one of the fundamental blocks of a smart CMOS imager currently under design, implements isotropic Gaussian filtering by means of a MOS-based RC network. Alternatively, this filtering can be turned into anisotropic by a very simple voltage comparator between neighboring nodes whose output controls the gate of the elementary MOS resistor. Anisotropic diffusion enables image enhancement by removing noise and small local variations while preserving edges. A binary edge image can be also attained by combining the output of the voltage comparators. In addition to these processing capabilities, the simulations have confirmed the robustness of the array against process variations and mismatch. The power consumption extrapolated for a VGA-resolution array processing images at 30fps is 570µW.

A signed pulse-train-based image processor-array for parallel kernel convolution in vision sensors

“…Low element count Kernel size limited to 3×3 [17,18] Event based processor Supports kernel sizes up to 32×32 elements Outputs are based on events, not suitable for transferring entire processed image to the output [19] RC smoothing network Arbitrary kernel size Only Gaussian kernels are supported [20] Bit serial binary processing with inter-pixel data shifting Arbitrary kernel size kernel coefficients limited to power of 2 [21] PWM processor with interpixel sample shifting Arbitrary kernel size and arbitrary positive kernel coefficients negative kernel coefficients cannot be processed…”

“…Event driven image sensors such as Camunas-Mesa et al (2012 are able to perform large kernel size convolutions but, as the result is transferred to the output using events they are more suitable for object based interpretations from the image rather than transferring of the raw filtered image to the output. Arbitrary kernel size convolution has been presented in Fernandez-Berni et al (2012), which uses an resistivecapacitive network expanded throughout the entire array to smooth-out the input image data. However the processor, although used to detect edges, is only capable of performing Gaussian convolutions and the kernel type is not programable.…”

An ultra-low-power voltage-mode asynchronous WTA-LTA circuit

“…The signal range for the pixels is [0.75V,1.5V]. The lower limit could be extended, but we constrain ourselves to this range for compatibility with other functional blocks already designed for this technology [13]. For the sake of a better visualization, simulation results for only three cells interconnected are shown in Fig.…”

A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study

“…Built-in devices for detecting persons can be based on the use of image sensors, similarly as described in solutions [32][33][34], in the form of a smart camera sensor with the function of preprocessing image data into binary images with white dots indicating the position of a person in the scene, a smart camera sensor for detecting the background of the scene and foreground of the scene as the position of the found person as in Reference [35], smart camera sensor with the function of neglecting the dynamic background as in Reference [36], a smart camera sensor for Histogram of Oriented Gradients (HOG) image data processing as in Reference [37], or a specialized solution of the System on Chip (SOC) coping with basic image processing tasks such as edge detection in References [38,39], as an edge detector [40], or a solution with a low-power smart CMOS image sensor used to detect persons for indoor and outdoor use as in Reference [41]. Other image processing solutions, such as edge detection using digital parallel pulse computation [42], a non-parallel Sobel edge detector addressed by a smart camera sensor [43], discuss similar solutions that serve as sources of information providing a wide range of possible alternative solutions.…”