  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.
