Search 닫기

The Korea Institute of Information and Commucation Engineering 2010; 8(3): 323-327

Published online June 30, 2010

https://doi.org/10.6109/jicce.2010.8.3.323

© Korea Institute of Information and Communication Engineering

Accelerating the Retinex Algorithm with CUDA

Seo, Hyo-Seok;Kwon, Oh-Young;

Department of electrical and electronic engineering, Korea University of Technology and Education, School of Computer Science and Engineering, Korea University of Technology and Education

Received: April 8, 2010; Accepted: April 28, 2010

Abstract

Recently, the television market trend is change to HD television and the need of the study on HD image enhancement is increased rapidly. To enhancement of image quality, the retinex algorithm is commonly used. That's why we studied how to accelerate the retinex algorithm with CUDA on GPGPU (general purpose graphics processing unit). Calculating average part in retinex algorithm is similar to pyramidal calculation. We parallelize this recursive pyramidal average calculating for all layers, map the average data into the 2D plane and reduce the calculating time dramatically. Sequential C code takes 8948ms to get the average values for all layers in $1024{times}1024$ image, but proposed method takes only only about 0.9ms for the same image. We are going to study about the real-time HD video rendering and image enhancement.

Keywords CUDA,GPGPU,Image enhancement,Pyramidal calculation,Retinex

Article

The Korea Institute of Information and Commucation Engineering 2010; 8(3): 323-327

Published online June 30, 2010 https://doi.org/10.6109/jicce.2010.8.3.323

Copyright © Korea Institute of Information and Communication Engineering.

Accelerating the Retinex Algorithm with CUDA

Seo, Hyo-Seok;Kwon, Oh-Young;

Department of electrical and electronic engineering, Korea University of Technology and Education, School of Computer Science and Engineering, Korea University of Technology and Education

Received: April 8, 2010; Accepted: April 28, 2010

Abstract

Recently, the television market trend is change to HD television and the need of the study on HD image enhancement is increased rapidly. To enhancement of image quality, the retinex algorithm is commonly used. That's why we studied how to accelerate the retinex algorithm with CUDA on GPGPU (general purpose graphics processing unit). Calculating average part in retinex algorithm is similar to pyramidal calculation. We parallelize this recursive pyramidal average calculating for all layers, map the average data into the 2D plane and reduce the calculating time dramatically. Sequential C code takes 8948ms to get the average values for all layers in $1024{times}1024$ image, but proposed method takes only only about 0.9ms for the same image. We are going to study about the real-time HD video rendering and image enhancement.

Keywords: CUDA,GPGPU,Image enhancement,Pyramidal calculation,Retinex

JICCE
Dec 31, 2024 Vol.22 No.4, pp. 267~343

Stats or Metrics

Share this article on

  • line

Journal of Information and Communication Convergence Engineering Jouranl of information and
communication convergence engineering
(J. Inf. Commun. Converg. Eng.)

eISSN 2234-8883
pISSN 2234-8255