一种基于BP神经网络的低分辨率图像复原方法

摘要

In order to investigate the restoration of low resolution images, the linear and nonlinear interpolation methods were applied for the interpolation of the common information matrix obtained from a series of pictures, getting the restructuring matrix. The characteristic block with the best restoration effect was determined by analyzing the pixel difference of the common information of each image at the same position. Then the characteristic blocks and their original blocks were used to build and train neural network. Finally, images were restored by the neural network and the differences between pictures were reduced. Experimental results showed that this method could significantly improve the efficiency and precision of algorithm.%为了研究低分辨率图像的复原问题,文中选取线性和非线性插值法对多帧图像的公共信息矩阵进行插值,得到重构矩阵.通过对每帧图像公共信息同一位置像素差异的分析,确定还原效果最好的特征块.利用这些特征块和对应的原特征块,构筑并训练神经网络,再由该神经网络重新还原每一帧图像,从而缩小每一帧公共信息之间的细节差异,达到最好的还原效果.试验表明,该方法能够很大程度能够提高算法的运算速度和运算精度.