CAJ | 학술논문

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50000、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。
위진일보제고도상삽치질량，봉부비선성도상삽치산법연구，제출일충간화적신경망락삽치산법。이용전향반궤신경망락(BP-NN)구조최가적도상삽치모형，통과2조실험학정해모형적BP망락최가탁복결구、최가채양모형화채양점수량，병정량묘술매조모형적모시。실험결과표명，대512×512상소도상채용BP-NN훈련점수량위50000、탁복결구위8-16-1적삼수삽치시，해산법평균삽치시간약위0.7 s，차기봉치신조비비선성균치방법평균고1 dB~2 dB，능구득도경가적시각감수。
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) 1dB~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.