摘要
传统的图像精度深度优化方法优化后的图像精度仍然较差,为此设计一种基于卷积神经网络的图像精度深度优化方法。采用目标监测方法提取图像目标区域特征,对图像的原始特征提取,利用深度学习框架生成多个特征图表示图像信息,并对图像像素集分割,固定待提高精度的图像,利用卷积神经网络修复图像,以实现图像增强,完成基于卷积神经网络的图像精度深度优化。实验对比结果表明,此次设计的基于卷积神经网络的图像精度深度优化方法比传统的优化方法优化后的图像精度高,具有较强的实用价值。
The traditional image precision depth optimization method still has poor image precision after optimization,so a convolution neural network based image precision depth optimization method is designed.The target region feature of image is extracted by target monitoring method,and the original feature of image is extracted.Multiple feature maps are generated by deep learning framework to represent image information,and the image pixel set is segmented to fix the image to be improved.The convolution neural network is used to repair the image to realize image enhancement,and the image precision depth optimization based on convolution neural network is completed.The experimental results show that the image precision depth optimization method based on convolution neural network is higher than the traditional optimization method,and has strong practical value.
作者
蒋平
JIANG Ping(Open Education College,Anhui Open University,Hefei 230022,China)
出处
《淮阴工学院学报》
CAS
2021年第3期30-34,共5页
Journal of Huaiyin Institute of Technology
关键词
卷积神经网络
精度
重构
识别
分割
convolutional neural network
accuracy
reconstruction
recognition
segmentation
作者简介
蒋平(1983-),男,江苏淮安人,讲师,硕士,主要从事人工智能研究。
