摘要
Abstract
When restoring blurred images of the moving objects,the traditional residual networks often suffer from insufficient feature extraction and noise interference in the case of serious blur occurs,so that the restored images fail to fully achieve the sharpness and details of the original images.Therefore,an improved residual network based method for restoring blurred images of moving objects is proposed.For the collected blurred images of moving objects,a multi loss function fusion method is adopted to improve the traditional residual block structure,construct an encoder-decoder network training structure,train the loss function,and enhance the feature learning ability of the networks.By the trained network,the restoration results of blurred images of the moving objects are output.The experimental results show that the peak signal-to-noise ratio(PSNR)of the restored blurred images of the moving objects is higher than 30 dB,and the image structural similarity is higher than 0.9.关键词
改进残差网络/运动目标/多损失函数融合/模糊图像/编辑器-解码器网络/复原方法Key words
improved residual network/moving object/multi loss function fusion/blurred image/encoder-decoder network/restoration method分类
信息技术与安全科学
