The traditional ingle-scale U-Net often leads to the loss of spatial
information during deblurring, which affects the deblurring accracy.
Additionally, due to the convolutional method's limitation in capturing
long-range dependencies, the quality of the recovered image is degraded. To
address the above problems, an asymmetric multiple scales U-net based on
self-attention (AMSA-UNet) is proposed to improve the accuracy and
computational complexity. By introducing a multiple-scales U shape
architecture, the network can focus on blurry regions at the global level and
better recover image details at the local level. In order to overcome the
limitations of traditional convolutional methods in capturing the long-range
dependencies of information, a self-attention mechanism is introduced into the
decoder part of the backbone network, which significantly increases the model's
receptive field, enabling it to pay more attention to semantic information of
the image, thereby producing more accurate and visually pleasing deblurred
images. What's more, a frequency domain-based computation method was introduced
to reduces the computation amount. The experimental results demonstrate that
the proposed method exhibits significant improvements in both accuracy and
speed compared to eight excellent methods

提出了一种基于自注意力的不对称多尺度 U 型网络（AMSA-UNet）来提高去模糊的准确性和计算复杂度，通过引入多尺度 U 形结构，在全局层面关注模糊区域，在局部层面更好地恢复图像细节；在解码器部分引入自注意力机制，显著增加了模型的感受野，使其对图像的语义信息更加关注，从而产生更准确和视觉上令人愉悦的去模糊图像；同时，引入了基于频域的计算方法来减少计算量。实验证明，与八种优秀方法相比，所提出的方法在准确性和速度方面都有显著提升。