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In this work, we present Eformer - Edge enhancement based transformer, anovel architecture that builds an encoder-decoder network using transformerblocks for medical image denoising. Non-overlapping window-based self-attentionis used in the transformer block that reduces computational requirements. Thiswork further incorporates learnable Sobel-Feldman operators to enhance edges inthe image and propose an effective way to concatenate them in the intermediatelayers of our architecture. The experimental analysis is conducted by comparingdeterministic learning and residual learning for the task of medical imagedenoising. To defend the effectiveness of our approach, our model is evaluatedon the AAPM-Mayo Clinic Low-Dose CT Grand Challenge Dataset and achievesstate-of-the-art performance, $i.e.$, 43.487 PSNR, 0.0067 RMSE, and 0.9861SSIM. We believe that our work will encourage more research intransformer-based architectures for medical image denoising using residuallearning.

Eformer: Edge Enhancement based Transformer for Medical Image Denoising