FPGA-based Scalable Road Image Stochastic Denosing Approach

被引：0

作者：

Park, Cheolhyeong ^{[1
]}

Kim, Kyung-Ki ^{[2
]}

Kim, Yong-Bin ^{[3
]}

Choi, Minsu ^{[4
]}

机构：

[1] Keimyung Univ, Dept Comp Engn, Daegu, South Korea

[2] Daegu Univ, Dept Elctron Engn, Gyongsan, South Korea

[3] Northeastern Univ, Dept ECE, Boston, MA 02115 USA

[4] Missouri Univ Sci & Technol, Dept ECE, Rolla, MO 65409 USA

来源：

18TH INTERNATIONAL SOC DESIGN CONFERENCE 2021 (ISOCC 2021) | 2021年

关键词：

Image Denoising; Stochastic Number Generator; FPGA;

D O I：

10.1109/ISOCC53507.2021.9613963

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work proposes FPGA-based stochastic computing for efficient and scalable road image stochastic denoising. Stochastic number generators were compared for fields requiring data reliability such as automotive vehicles. The denoising performance of PRNG (Pseudo-Random Number Generator) and LDSG (Low Discrepancy Sequence Generator) are showed in noised KETTI dataset with filtering algorithms. The proposed approach is expected to be easily applied to SoCs with embedded FPGA for lightweight embedded implementation of image denoising algorithms.

引用

页码：351 / 352

页数：2

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