In this work, we present Eformer - Edge enhancement based transformer, a
novel architecture that builds an encoder-decoder network using transformer
blocks for medical image denoising. Non-overlapping window-based self-attention
is used in the transformer block that reduces computational requirements. This
work further incorporates learnable Sobel-Feldman operators to enhance edges in
the image and propose an effective way to concatenate them in the intermediate
layers of our architecture. The experimental analysis is conducted by comparing
deterministic learning and residual learning for the task of medical image
denoising. To defend the effectiveness of our approach, our model is evaluated
on the AAPM-Mayo Clinic Low-Dose CT Grand Challenge Dataset and achieves
state-of-the-art performance, $i.e.$, 43.487 PSNR, 0.0067 RMSE, and 0.9861
SSIM. We believe that our work will encourage more research in
transformer-based architectures for medical image denoising using residual
learning.

本文提出了 Eformer - Edge enhancement based transformer，这是一种新型的结构，利用变压器块构建编码器 - 解码器网络进行医学图像去噪。变压器块中使用非重叠窗口自注意力，以减少计算要求。该文进一步将可学习的 Sobel-Feldman 运算符并入图像中以增强边缘，并提出了将它们在我们的结构的中间层中连接的有效方法。实验分析比较了确定性学习和残差学习用于医学图像去噪任务。为了证明我们方法的有效性，我们的模型在 AAPM-Mayo Clinic 低剂量 CT 大挑战数据集上进行评估，并达到最新的性能，即 43.487 PSNR，0.0067 RMSE 和 0.9861 SSIM。我们相信我们的工作将鼓励更多的研究者使用残差学习开发基于变压器结构的医学图像去噪方法。