一种基于深度残差网络的图像修复算法设计与实现

Design and implementation of an image inpainting algorithmbased on deep residual network

随着数字图像在人们日常生活中使用越来越多,针对破损图像缺失区域的数字图像修复问题,本文提出一种基于深度残差网络的图像修复模型。该模型总体架构基于编码-解码结构,编码器采用不同深度的残差网络,解码器分别使用反卷积网络结构和上采样-卷积结构。通过实验探讨在本模型中不同结构的解码器、编码器以及不同的损失函数对图像修复效果的影响。实验结果表明:本文提出的基于深度残差网络的图像修复模型,采用修改后的Resnet 34-layer作为编码器,反卷积网络作为解码器,L1 Loss作为损失函数,能够达到较好的图像修复效果。

With the increasing use of digital images in people's daily life,restoring the missing areas of damaged images has become a problem,for which this paper proposes an image inpainting model based on a deep residual network.The overall architecture of the model is based on the encoder-decoder structure.The encoder uses residual networks of different depths,and the decoder uses a deconvolution network structure and an upsampling-convolution structure.The effects of different structure decoders,encoders and different loss functions on the image inpainting effect in this model were discussed through experiments.The experimental results showed that the image inpainting model based on the deep residual network proposed in this paper could achieve good image inpainting effect by adopting the modified Resnet 34-layer as the encoder,the deconvolution network as the decoder,and the L1 Loss as the loss function.