双路多尺度残差网络的图像超分辨率重建

Image Super-resolution Reconstruction Based on Binary Channels Multi-scale Residual Network

针对传统单幅图像超分辨率重建方法出现的边缘特征模糊问题,提出了一种双路多尺度残差网络(BMRN)的重建方法。首先直接对低分辨率图像进行特征提取,较大程度保留特征信息;再构建多个独立的双路多尺度残差网络提取高频信息,其中残差连接的引入可以有效解决网络加深导致的梯度消失问题,双路多尺度结构可以相互补充卷积中的尺度信息,改善网络中的信息流;最后通过上采样操作,得到重建高分辨率图像。在数据集Set5上进行的×3尺度的重建结果表明:与Bicubic、SRCNN和VDSR等传统方法相比,BMRN的平均PSNR/SSIM分别提高了3.37 dB/0.0532、1.01 dB/0.0124和0.09 dB/0.0004。实验数据充分说明:BMRN能够较好的恢复图像轮廓特征,获得了较高的PSNR、SSIM和更好的视觉效果。

In order to solve the problem of fuzzy edge features in traditional single image super-resolution reconstruction method,a binary channel multi-scale residual network(BMRN)reconstruction method is proposed.Firstly,the low-resolution image features are extracted directly to retain the feature information to a large extent.Secondly,several independent dual channel multi-scale residual networks are constructed to extract high-frequency information.The introduction of residual connection can effectively solve the problem of gradient disappearance caused by network deepening,and the binary channel multi-scale structure can complement the scale information in convolution to improve the information flow in the network;finally,the multi-scale residual network can be used to extract the high-frequency information through the up sampling operation,the reconstructed high-resolution image is obtained.The 3-scale reconstruction results on Set5 show that the average PSNR/SSIM of BMRN is 3.37 dB/0.0532,1.01 dB/0.0124 and 0.09 dB/0.0004 respectively compared with traditional algorithms such as Bicubic,SRCNN and VDSR.The experimental data fully show that BMRN can restore the image contour features,and obtain higher PSNR,SSIM and better visual effect.