WTransU-Net: Wiener deconvolution meets multi-scale transformer-based U-net for image deblurring

Deblurring is a classical image restoration problem. Although recent methods have shown promising deblurring performance, most methods still cannot effectively balance the texture details restoration and model complexity. In order to improve the performance of deblurring, some models are designed to be more complex. In this work, a simple and efficient Wiener deconvolution and multi-scale transformer-based U-Net (WTransU-Net) is proposed to tackle these problems. First, the proposed Wiener feature extraction module uses explicit Wiener deconvolution to extract the Wiener features in the deep feature space. Then, the obtained Wiener features are input into a multi-scale feature reconstruction module which only embeds one transformer refining block in each scale of the U-Net to deblur the image from local and global perspectives. In addition, a multi-scale hybrid loss function is designed to train the WTransU-Net in an end-to-end manner to better learn the content and texture details. The experimental results on benchmark datasets show that compared with the state-of-the-art deblurring methods, the proposed WTransU-Net can achieve better performance with fewer artifacts in terms of quantitatively and qualitatively.