# EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

arXiv: https://arxiv.org/abs/2011.00139 · HTML: https://mlsanity.com/paper/2011.00139/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 26 of 26)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV](https://mlsanity.com/paper/2407.11087/) (2024)
- [Eformer: Edge Enhancement based Transformer for Medical Image Denoising](https://mlsanity.com/paper/2109.08044/) (2021)
- [Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction](https://mlsanity.com/paper/2111.14831/) (2021)
- [ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising](https://mlsanity.com/paper/2307.12225/) (2023)
- [LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring](https://mlsanity.com/paper/2302.10630/) (2023)
- [DenoMamba: A fused state-space model for low-dose CT denoising](https://mlsanity.com/paper/2409.13094/) (2024)
- [Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation](https://mlsanity.com/paper/2304.04581/) (2023)
- [Region Attention Transformer for Medical Image Restoration](https://mlsanity.com/paper/2407.09268/) (2024)
- [CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising](https://mlsanity.com/paper/2411.07930/) (2024)
- [LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models](https://mlsanity.com/paper/2507.06140/) (2025)
- [TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2512.14550/) (2025)
- [Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography](https://mlsanity.com/paper/2304.10839/) (2023)
- [Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers](https://mlsanity.com/paper/2403.03551/) (2024)
- [A Denoising Framework for Real-World Ultra-Low-Dose Lung CT Images Based on an Image Purification Strategy](https://mlsanity.com/paper/2510.07492/) (2025)
- [MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising](https://mlsanity.com/paper/2603.02012/) (2026)
- [MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data](https://mlsanity.com/paper/2309.03686/) (2023)
- [Image Denoising via Style Disentanglement](https://mlsanity.com/paper/2309.14755/) (2023)
- [Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows](https://mlsanity.com/paper/2410.17543/) (2024)
- [Prompt Guiding Multi-Scale Adaptive Sparse Representation-driven Network for Low-Dose CT MAR](https://mlsanity.com/paper/2504.19687/) (2025)
- [Toward Better Optimization of Low-Dose CT Enhancement: A Critical Analysis of Loss Functions and Image Quality Assessment Metrics](https://mlsanity.com/paper/2511.00698/) (2025)
- [3D Wavelet-Based Structural Priors for Controlled Diffusion in Whole-Body Low-Dose PET Denoising](https://mlsanity.com/paper/2601.07093/) (2026)
- [IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoising](https://mlsanity.com/paper/2602.19314/) (2026)
- [CARE: Training-Free Controllable Restoration for Medical Images via Dual-Latent Steering](https://mlsanity.com/paper/2603.25026/) (2026)
- [DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling](https://mlsanity.com/paper/2606.28453/) (2026)
- [A deep dictionary network-based foundation model for ultra-low-dose CT denoising](https://mlsanity.com/paper/2609.16031/) (2026)

## Abstract (arXiv)

In the past few decades, to reduce the risk of X-ray in computed tomography (CT), low-dose CT image denoising has attracted extensive attention from researchers, which has become an important research issue in the field of medical images. In recent years, with the rapid development of deep learning technology, many algorithms have emerged to apply convolutional neural networks to this task, achieving promising results. However, there are still some problems such as low denoising efficiency, over-smoothed result, etc. In this paper, we propose the Edge enhancement based Densely connected Convolutional Neural Network (EDCNN). In our network, we design an edge enhancement module using the proposed novel trainable Sobel convolution. Based on this module, we construct a model with dense connections to fuse the extracted edge information and realize end-to-end image denoising. Besides, when training the model, we introduce a compound loss that combines MSE loss and multi-scales perceptual loss to solve the over-smoothed problem and attain a marked improvement in image quality after denoising. Compared with the existing low-dose CT image denoising algorithms, our proposed model has a better performance in preserving details and suppressing noise.