mlsanityData to 14 Oct 2026

Eformer: Edge Enhancement based Transformer for Medical Image Denoising

arXiv 2109.08044 ↗First posted 16 Sep 2021eess.IV cs.CVcited by 30

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Abstract (arXiv)

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.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 16 Sep 2021.

Builds on

14 of 27 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2020Pre-Trained Image Processing Transformercited by 893
  5. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 866
  6. 2020Learning Texture Transformer Network for Image Super-Resolutioncited by 347
  7. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  8. 2016A guide to convolution arithmetic for deep learningcited by 185
  9. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 162
  10. 2021Colorization Transformercited by 89
  11. 2016Medical image denoising using convolutional denoising autoencoderscited by 76
  12. 20183D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Networkcited by 47
2 more
  1. 2020EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoisingcited by 26
  2. 2019Deep Learning for Low-Dose CT Denoisingcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 12; 2023: 9; 2024: 4; 2025: 2; 2026: 1.

30 papers.

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  1. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 273
  2. 2022Transformers in Medical Imaging: A Surveycited by 245
  3. 2022CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoisingcited by 46
  4. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  5. 2024Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKVcited by 36
  6. 2022Class-Aware Adversarial Transformers for Medical Image Segmentationcited by 33
  7. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 33
  8. 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
  9. 2024All-In-One Medical Image Restoration via Task-Adaptive Routingcited by 30
  10. 2023Transformers in Healthcare: A Surveycited by 17
  11. 2021CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translationcited by 15
  12. 2021Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstructioncited by 15
18 more
  1. 2023Self-Supervised Image Denoising for Real-World Images with Context-aware Transformercited by 13
  2. 2024DenoMamba: A fused state-space model for low-dose CT denoisingcited by 11
  3. 2023Real-World Denoising via Diffusion Modelcited by 10
  4. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  5. 2022Vision Transformers in Medical Imaging: A Reviewcited by 8
  6. 2023Full-resolution MLPs Empower Medical Dense Predictioncited by 6
  7. 2022A Transformer-based Network for Deformable Medical Image Registrationcited by 3
  8. 2025TAT: Task-Adaptive Transformer for All-in-One Medical Image Restorationcited by 3
  9. 2026CineMatte: Background Matting for Virtual Production and Beyondcited by 3
  10. 2023A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancementcited by 2
  11. 2024Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstructioncited by 2
  12. 2022PTGCF: Printing Texture Guided Color Fusion for Impressionism Oil Painting Style Renderingcited by 1
  13. 2022Deep is a Luxury We Don't Havecited by 1
  14. 2023Hierarchical Disentangled Representation for Invertible Image Denoising and Beyondcited by 1
  15. 2023Whole-body PET image denoising for reduced acquisition timecited by 1
  16. 2023XVertNet: Unsupervised Contrast Enhancement of Vertebral Structures with Dynamic Self-Tuning Guidance and Multi-Stage Analysiscited by 0
  17. 2023MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Datacited by 0
  18. 2025Multi-Plane Vision Transformer for Hemorrhage Classification Using Axial and Sagittal MRI Datacited by 0