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Single image super resolution via wavelet transform fusion and SRFeat network
Journal of Ambient Intelligence and Humanized Computing ( IF 3.662 ) Pub Date : 2020-05-16 , DOI: 10.1007/s12652-020-02065-0
Chunyan Ma , Junwu Zhu , Yujie Li , Jianru Li , Yi Jiang , Xin Li

Image super resolution is a vital research topic in the field of computer vision. It aims to reconstruct high resolution images from low resolution images. Although the conventional image super resolution methods have achieved good performance and effect, there are still have some issues, e.g., the high-frequency details information is insufficient, and the reconstruction process will bring additional noise, and most basic interpolation techniques produce blurry results. To settle the problems mentioned above, we consider combining the deep learning method with the frequency domain fusion method. In this paper, a novel single image super resolution method based on SRFeat network and wavelet fusion is proposed. First, the training image is taken as the input of the backbone SRFeat network, then the generative adversarial network training is carried out. Then, the up-sampling is utilized to obtain the coarse super resolved image. Finally, the output image after the network training is combined with the up-sampling image of the low-resolution image by Wavelet fusion to obtain the final result. Without increasing the depth of the network and the redundant parameters, the proposed method can achieve better reconstruct result. The experimental results show that the proposed method can not only reduce the probability of image distortion, but recover the global information of the reconstructed image and remove the noise brought by the reconstruction process. The PSNR value of the proposed method is improved 0.3 dB, and the SSIM is improved 0.02.



中文翻译:

通过小波变换融合和SRFeat网络实现单图像超分辨率

图像超分辨率是计算机视觉领域的重要研究课题。它旨在从低分辨率图像重建高分辨率图像。尽管传统的图像超分辨率方法已经取得了良好的性能和效果,但是仍然存在一些问题,例如高频细节信息不足,重构过程会带来额外的噪声,大多数基本的插值技术会产生模糊的结果。为了解决上述问题,我们考虑将深度学习方法与频域融合方法相结合。提出了一种基于SRFeat网络和小波融合的单图像超分辨率新方法。首先,将训练图像作为骨干SRFeat网络的输入,然后进行生成对抗网络训练。然后,利用上采样获得粗糙的超分辨图像。最后,通过小波融合将网络训练后的输出图像与低分辨率图像的上采样图像进行组合,以获得最终结果。该方法在不增加网络深度和冗余参数的情况下,可以获得较好的重建效果。实验结果表明,该方法不仅可以降低图像失真的几率,而且可以恢复重建图像的全局信息,消除重建过程带来的噪声。该方法的PSNR值提高了0.3 dB,SSIM提高了0.02。通过小波融合将网络训练后的输出图像与低分辨率图像的上采样图像进行组合,得到最终结果。该方法在不增加网络深度和冗余参数的情况下,可以获得较好的重建效果。实验结果表明,该方法不仅可以降低图像失真的几率,而且可以恢复重建图像的全局信息,消除重建过程带来的噪声。该方法的PSNR值提高了0.3 dB,SSIM提高了0.02。通过小波融合将网络训练后的输出图像与低分辨率图像的上采样图像进行组合,得到最终结果。该方法在不增加网络深度和冗余参数的情况下,可以获得较好的重建效果。实验结果表明,该方法不仅可以降低图像失真的几率,而且可以恢复重建图像的全局信息,消除重建过程带来的噪声。该方法的PSNR值提高了0.3 dB,SSIM提高了0.02。该方法可以取得较好的重建效果。实验结果表明,该方法不仅可以降低图像失真的可能性,而且可以恢复重建图像的全局信息,消除重建过程中产生的噪声。该方法的PSNR值提高了0.3 dB,SSIM提高了0.02。该方法可以取得较好的重建效果。实验结果表明,该方法不仅可以降低图像失真的几率,而且可以恢复重建图像的全局信息,消除重建过程带来的噪声。该方法的PSNR值提高了0.3 dB,SSIM提高了0.02。

更新日期:2020-05-16
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