采用深度学习的快速超分辨率图像重建方法

Fast Super-Resolution Image Reconstruction Method Using Deep Learning

为满足实际工业生产需要,提出一种基于深度学习的快速超分辨率图像重建方法.采用一种快速的卷积神经网络结构,使用级联的小卷积核以取得重建速度上的提升,加深卷积网络以取得重建质量上的提升.实验结果表明:在标准的公共数据集上,该算法重建的高分辨率图像在主观视觉感受和客观的图像质量评价(峰值信噪比)上取得较好的效果,且重建时间大大缩短;将算法应用在实际的项目中,能达到阈值分割后准确检测物体的标准,减少企业对高额工业相机的经济开支.

In order to meet the needs of actual industrial production,a fast super-resolution image reconstruction method based on deep learning is proposed.We proposed our own convolutional neural network structure,using cascaded small convolution kernels to achieve a higher reconstruction speed,and deepening the convolution network to achieve an improvement in reconstruction quality.The experimental results show that on the standard public dataset,the high-resolution image reconstructed by the our algorithm achieves better results in subjective visual perception and objective image quality evaluation(peak signal-to-noise ratio),at the mean time,the reconstruction time is greatly shortened.The algorithm is applied in projects to solve the problem for accurately detecting objects after threshold segmentation.In this way,it also reduces the high expenses of enterprises for purchasing industrial cameras.