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

• URL: http://arxiv.org/abs/2011.00139v1
• Date: Fri, 30 Oct 2020 23:12:09 GMT
• Title: EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
• Authors: Tengfei Liang, Yi Jin, Yidong Li, Tao Wang, Songhe Feng, Congyan Lang
• Abstract summary: We propose the Edge enhancement based Densely connected Convolutional Neural Network (EDCNN) We construct a model with dense connections to fuse the extracted edge information and realize end-to-end image denoising. Compared with the existing low-dose CT image denoising algorithms, our proposed model has a better performance in preserving details and suppressing noise.
• Score: 27.86840312836051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression [58.618625678054826]
  This study presents an enhanced neural compression method designed for optimal visual fidelity. We have trained our model with a sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual loss, style loss, and a non-binary adversarial loss. Our empirical findings demonstrate that this approach significantly improves the statistical fidelity of neural image compression.
  arXiv  Detail & Related papers  (2024-01-25T08:11:27Z)
• Multi-stage image denoising with the wavelet transform [125.2251438120701]
  Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. We propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and residual block (RB)
  arXiv  Detail & Related papers  (2022-09-26T03:28:23Z)
• Unsupervised Denoising of Optical Coherence Tomography Images with Dual_Merged CycleWGAN [3.3909577600092122]
  We propose a new Cycle-Consistent Generative Adversarial Nets called Dual-Merged Cycle-WGAN for retinal OCT image denoiseing. Our model consists of two Cycle-GAN networks with imporved generator, descriminator and wasserstein loss to achieve good training stability and better performance.
  arXiv  Detail & Related papers  (2022-05-02T07:38:19Z)
• Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis [148.16279746287452]
  We propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise. Experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-24T18:11:31Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Deep neural networks-based denoising models for CT imaging and their efficacy [0.3058685580689604]
  We aim to examine the image quality of the Deep Neural Networks (DNNs) results from a holistic viewpoint for low-dose CT image denoising. We build a library of advanced DNN denoising architectures such as the DnCNN, U-Net, Red-Net, GAN, etc. Each network is modeled, as well as trained, such that it yields its best performance in terms of the PSNR and SSIM.
  arXiv  Detail & Related papers  (2021-11-18T06:18:26Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• Exploring ensembles and uncertainty minimization in denoising networks [0.522145960878624]
  We propose a fusion model consisting of two attention modules, which focus on assigning the proper weights to pixels and channels. The experimental results show that our model achieves better performance on top of the baseline of regular pre-trained denoising networks.
  arXiv  Detail & Related papers  (2021-01-24T20:48:18Z)
• Image Denoising using Attention-Residual Convolutional Neural Networks [0.0]
  We propose a new learning-based non-blind denoising technique named Attention Residual Convolutional Neural Network (ARCNN) and its extension to blind denoising named Flexible Attention Residual Convolutional Neural Network (FARCNN) ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for Gaussian and Poisson denoising, respectively FARCNN presented very consistent results, even with slightly worsen performance compared to ARCNN.
  arXiv  Detail & Related papers  (2021-01-19T16:37:57Z)
• Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising [20.965610734723636]
  Deep learning-based technique has emerged as a dominant method for low dose CT(LDCT) denoising. In this study, we propose a novel convolutional module as the first attempt to utilize neighborhood similarity of CT images for denoising tasks. Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising.
  arXiv  Detail & Related papers  (2020-11-11T10:44:52Z)
• Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising [80.10533234415237]
  This paper presents a multi-level wavelet residual network (MWRN) architecture as well as a progressive training scheme to improve image denoising performance. Experiments on both synthetic and real-world noisy images show that our PT-MWRN performs favorably against the state-of-the-art denoising methods.
  arXiv  Detail & Related papers  (2020-10-23T14:14:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.