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A superparallel image filtering digital-pixel-sensor employing a compressive multiplication technique

Authors: Hongbo Zhu; Kunihiro Asada;

A superparallel image filtering digital-pixel-sensor employing a compressive multiplication technique

Abstract

A full-pixel parallel image filtering architecture is developed based on the digital-pixel-sensor. A compressive multiplication technique is employed to accelerate the processing speed. As a result, speed-ups from 3.2 to 5.2 were achieved for Gaussian kernels ranged from 5×5 to 15×15 in scale-invariant feature transform (SIFT) algorithm. A 108 × 96-pixel sensor was designed using a 0.18 μm CMOS process in a 5 mm×5 mm chip. By simulating the sensor at 100 MHz, the image filtering times for 5×5, 7×7, and 9×9 Gaussian kernels in the SIFT algorithm are 34 μs, 49 μs, and 83 μs, respectively. Such a high processing speed is very important for achieving the real-time performance when filtering high resolution images with large kernels.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
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