Implementation of Image Enhancement Algorithms and Recursive Ray Tracing using CUDA

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• 作者：Mr. Diptarup Saha ; Mr. Karan Darji ; Narendra Patel ; Dr. ; Darshak Thakore ; Dr.
• 关键词：CUDA ; Image Processing ; NVIDIA GPU ; Parallel Programming
• 刊名：Procedia Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：79
• 期：Complete
• 页码：516-524
• 全文大小：807 K

文摘

This paper intends to achieve high performance in terms of time by implementing various time consuming application on NVIDIA Graphics Processing Unit (GPU) by using parallel programming model NVIDIA Compute Unified Device Architecture (CUDA). NVIDIA CUDA provides platform for developing parallel applications on NVIDIA GPUs. So it gives developers a platform to build high-end parallel processing applications. This paper implements various image processing algorithms on both Central Processing Unit (CPU) and GPU. Implemented point-to-point image processing algorithms are brightening filter, darkening filter, negative filter and RGB to Grayscale filter. Along with various convolution algorithms that consider value of its neighboring pixels are also implemented. Implemented convolution algorithms are sobel filter for edge detection, low pass filter and high pass filter. Performance analysis of the implemented image processing algorithms is done on both CPU and GPU. Analysis is made on images of resolution 3000 X 3000. Color-ed images are used for point-to-point pixel processing algorithms. Grayscale images are used for all convolution algorithms. Performance analysis done for point-to-point processing algorithms by varying number of threads per block.

Recursive ray tracing is also implemented on GPU, and found performance gain compare to serial algorithm run on CPU.