一种分区BP人工神经网络图像差值算法

为了提高图像插值质量，利用前向反馈人工神经网络（BP-ANN）的自学习、自适应和泛化能力，开展分区BP-ANN图像差值研究.将图像中待插值像素划分为光滑区和边缘区，每个区分别对应1个BP-ANN进行图像差值操作，并通过3组实验确定分区BP-ANN的网络结构、采点模式和插值流程.结果表明，采用8-16-1拓扑结构的BP-ANN算法可达到图像的可视化质量和时间之间的最佳平衡点；与经典线性均值（LA）图像插值算法相比，分区BP-ANN算法在保持最佳视觉效果的前提下，峰值性噪比高约0.593 8 dB.

Regional Image Interpolation Algorithms with Back-Propagation Artificial Neural Network

In order to improve the quality of image interpolation，back-propagation artificial neural network (BP-ANN) with self-learning，adaptive and generalization ability has been used to carry out the regional image interpolation research.Missing pixel in image is divided into smooth region and edge region，each region corresponding to a BP-ANN interpolation operation.To identify the topology structure，sampling mode and the interpolation process of regional BP-ANN，three experiments were performed.The experimental results show that best balance between CPU-time consumption and visual quality lies in 8-16-1 topology of BP-ANN;compared with the classical LA interpolation algorithm，our proposed algorithm provides 0.593 8 dB higher PSNR while performed better visual quality.