Dual-Channel Residual Network for Image Super-Resolution Reconstruction

doi:10.7652/xjtuxb202201018

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Dual-Channel Residual Network for Image Super-Resolution Reconstruction

更新时间：2025-05-08

- Dual-Channel Residual Network for Image Super-Resolution Reconstruction
- Vol. 56, Issue 1, Pages: 158-164(2022)
- 作者机构：
  
  长安大学信息工程学院,西安,710054
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202201018
  CLC： TP391.41
- Online First：10 January 2022，
  
  Published：2022
- 稿件说明：
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Dual-Channel Residual Network for Image Super-Resolution Reconstruction[J]. 2022, 56(1): 158-164.
DOI：

Dual-Channel Residual Network for Image Super-Resolution Reconstruction[J]. 2022, 56(1): 158-164. DOI： 10.7652/xjtuxb202201018.

摘要

针对现有基于深度学习的自然图像超分辨率算法在图像高频细节重建方面的不足

提出了一种更注重图像高频细节重建的双通道残差网络。使用带有通道注意力机制的残差结构作为网络的主通道; 为了在重建过程中更好地保留原始图像的几何结构和边缘信息

使用自适应结构化卷积设计了网络的辅助通道

以此构建的双通道残差网络在学习过程中会有更强的高频信息捕获能力; 为了使重建图像效果更加符合人眼的主观视觉感受

结合使用L

损失函数和多尺度结构相似度损失函数来训练网络

使网络在训练过程中能够较好地保留图像的视觉效果。实验结果表明:在主通道外并构基于结构化卷积的辅助通道可以使重建图像的峰值信噪比提高2 dB; 结合使用L

损失函数和多尺度结构相似度损失函数可以使重建图像的峰值信噪比提高3 dB、结构相似性提高0.5; 与同类网络客观定量相比

所提网络在两个公开数据集上取得的效果更优。

Abstract

Aiming at the shortcomings of the existing deep learning based image super-resolution algorithms in image high-frequency detail reconstruction

a dual-channel residual network emphasizing image high-frequency detail reconstruction is proposed. Residual structure with channel attention mechanism is leveraged as the main channel of the network. Targeting on retaining more delicate geometric structure and edge information of the original image during the reconstruction

the auxiliary channel of the network is designed by an adaptive structured convolution hence the evolved dual-channel residual network has a stronger ability to capture high-frequency information during the learning process. To make the reconstructed image we

ll coincide with the subjective visual experience of human eye

loss function is combined with the multi-scale structural similarity loss function to train the network

so that the network completely retains the visual effect of the image during the training process. Experiments on the benchmark database show that combining the auxiliary channel based on adaptive structured convolution outside the main channel can heighten the peak signal-to-noise ratio of the reconstructed image by 2 dB. The simultaneous operation of L

loss function and the multi-scale structural similarity loss function can heighten the peak signal-to-noise ratio of the reconstructed image by 3 dB and the structural similarity by 0.05. The objective and quantitative comparison with the competing networks exhibits the proposed network's outstanding effectiveness on two public data sets.

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references

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