Hardware acceleration of a neighborhood dependent component feature learning (NDCFL) super-resolution algorithm

Date of Award

2013

Degree Name

M.S. in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Advisor: Tarek Taha

Abstract

Image processing and computer vision algorithms allow computers to make sense of pictures and video seen through cameras. These have applications in a large variety of real time" applications like surveillance, intelligence gathering, robotics, automobile driving, aviation, etc., where the picture from the video needs to be processed by a computer as soon as it is taken. However these algorithms are time intensive because of its compute bound nature. In this literature, a single image super resolution algorithm based on Neighborhood Dependent Component Feature Learning (NDCFL) is accelerated by multiple GPUs and multiple CPU cores, using NVIDIA's Computer Unified Device Architecture (CUDA), OpenCV and POSIX threads. Given a low resolution input, this method uses image features to adaptively learn the regression kernel based on local covariance to estimate the high resolution image. The accelerated implementation performs at speed 51 times faster than that of original implementation for 590X580 frame, and achieves processing rate close to real-time."

Keywords

Computer algorithms Design, High resolution imaging Processing Computer programs, Image processing Computer programs, Computer engineering; electrical engineering; GPU; CUDA; GPGPU; super-resolution on GPU; acceleration of super-resolution; image Processing; NDCFL; super-resolution; GPU acceleration; accelerating image processing algorithm using GPU; multi-core acceleration

Rights Statement

Copyright © 2013, author

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