Title: Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution Show Chinese title

Title: 基于密集连接残差网络的单图像超分辨率双重建方法

Abstract: Deep learning-based single image super-resolution enables very fast and high-visual-quality reconstruction. Recently, an enhanced super-resolution based on generative adversarial network (ESRGAN) has achieved excellent performance in terms of both qualitative and quantitative quality of the reconstructed high-resolution image. In this paper, we propose to add one more shortcut between two dense-blocks, as well as add shortcut between two convolution layers inside a dense-block. With this simple strategy of adding more shortcuts in the proposed network, it enables a faster learning process as the gradient information can be back-propagated more easily. Based on the improved ESRGAN, the dual reconstruction is proposed to learn different aspects of the super-resolved image for judiciously enhancing the quality of the reconstructed image. In practice, the super-resolution model is pre-trained solely based on pixel distance, followed by fine-tuning the parameters in the model based on adversarial loss and perceptual loss. Finally, we fuse two different models by weighted-summing their parameters to obtain the final super-resolution model. Experimental results demonstrated that the proposed method achieves excellent performance in the real-world image super-resolution challenge. We have also verified that the proposed dual reconstruction does further improve the quality of the reconstructed image in terms of both PSNR and SSIM. Show Chinese abstract

Abstract: 基于深度学习的单图像超分辨率能够实现非常快速且视觉质量高的重建。 最近，一种基于生成对抗网络的增强超分辨率（ESRGAN）在重建高分辨率图像的定性和定量质量方面都取得了优异的性能。 在本文中，我们提出在两个密集块之间添加一个额外的快捷连接，以及在密集块内部的两个卷积层之间添加快捷连接。 通过在所提出的网络中添加更多快捷连接的简单策略，使得学习过程更快，因为梯度信息可以更容易地反向传播。 基于改进的ESRGAN，提出了双重建方法，以学习超分辨率图像的不同方面，从而有选择性地提高重建图像的质量。 在实践中，超分辨率模型仅基于像素距离进行预训练，然后根据对抗损失和感知损失对模型中的参数进行微调。 最后，通过加权求和两种不同模型的参数来融合它们，以获得最终的超分辨率模型。 实验结果表明，所提出的方法在真实世界图像超分辨率挑战中表现出色。 我们还验证了所提出的双重建方法在PSNR和SSIM方面进一步提高了重建图像的质量。