Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution

TL;DR

This paper introduces a dual reconstruction approach with a densely connected residual network for single image super-resolution, improving learning efficiency and image quality through enhanced network architecture and model fusion.

Contribution

It proposes adding additional shortcuts in a densely connected residual network and a dual reconstruction strategy to enhance super-resolution performance.

Findings

Improved PSNR and SSIM metrics on super-resolution tasks.

Faster learning process due to enhanced gradient back-propagation.

Achieved state-of-the-art results in real-world image super-resolution challenge.

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.