GPU based fast algorithm for tanner graph based image interpolation

Wei Lei, Ruiqin Xiong, Siwei Ma, Luhong Liang
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引用次数: 2

Abstract

In image/video processing software and hardware products, low complexity interpolation algorithms, such as cubic and splines methods, are commonly used. However, these methods tend to blur textures and produce jaggy effect compared with other adaptive methods such as NEDI, SAI. Tanner graph based image interpolation algorithm has better effect in dealing with edge and texture, but with high computation complexity. Thanks to the high performance parallel processing capability of today's GPU, use of complex algorithms for real time application is becoming possible. In this paper, we present a fast algorithm for tanner graph based image interpolation and it's implementation on GPU. In our algorithm, the image model training process of tanner graph based image interpolation is greatly simplified. Experimental results show that the GPU implementation can be more than 47 times as fast as the CPU implementation.
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基于GPU的tanner图图像插值快速算法
在图像/视频处理软件和硬件产品中,通常使用低复杂度的插值算法,如三次和样条方法。但与NEDI、SAI等其他自适应方法相比,这些方法容易使纹理模糊,产生锯齿效果。基于Tanner图的图像插值算法在处理边缘和纹理方面效果较好,但计算量较大。由于当今GPU的高性能并行处理能力,使用复杂的算法进行实时应用成为可能。本文提出了一种基于tanner图的快速图像插值算法及其在GPU上的实现。该算法极大地简化了基于tanner图插值的图像模型训练过程。实验结果表明,GPU实现的速度是CPU实现的47倍以上。
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