摘要
为进一步提高图像插值质量,丰富非线性图像插值算法研究,提出一种简化的神经网络插值算法。利用前向反馈神经网络(BP-NN)构造最佳的图像插值模型,通过2组实验确定该模型的BP网络最佳拓扑结构、最佳采样模型和采样点数量,并定量描述每组模型的耗时。实验结果表明,对512×512像素图像采用BP-NN训练点数量为50 000、拓扑结构为8-16-1的参数插值时,该算法平均插值时间约为0.7 s,且其峰值信噪比比线性均值方法平均高1 dB^2 dB,能够得到更佳的视觉感受。
The objective of this paper is to improve the quality of image interpolation, enrich non-linear image interpolation algorithm. This paper presents a simplified neural network interpolation algorithm, in which the Back Propagation Neural Network（BP-NN） is adopted to construct the best interpolation model. Two sets of experiments determine the best topology of the BP network＇s model, the optimal sampling model, the number of sampling points, and describe the time-consuming of each group model quantitatively. Experimental results show that a 512-512 pixel image interpolation using BP-NN can obtain the Peak Signal to Noise Radio（PSNR） ldB-2 Db higher than Linear Average（LA） method, while the number of pixels in training sets is 50 000 with the topology of 8-16-1. Therefore, the proposed algorithm performs better visual quality.
出处
《计算机工程》
CAS
CSCD
2013年第9期263-266,共4页
Computer Engineering
基金
国家自然科学基金资助项目(61262088
61063042
61363083)
新疆自然科学基金资助项目(2011211A011
2013211A011)
新疆高校科研计划基金资助项目(XJEDU2012110)
新疆大学博士启动基金资助项目(BS100128)
关键词
前向反馈神经网络
图像插值
峰值信噪比
采点模式
隐藏层神经元
线性插值
Back Propagation Neural Network（BP-NN）
image de-interlacing
Peak Signal to Noise Radio（PSNR）
sampling mode
hidden layer neuron
linear interpolation
