A CNN-Based Blind Denoising Method for Endoscopic Images

• URL: http://arxiv.org/abs/2003.06986v1
• Date: Mon, 16 Mar 2020 03:11:11 GMT
• Title: A CNN-Based Blind Denoising Method for Endoscopic Images
• Authors: Shaofeng Zou, Mingzhu Long, Xuyang Wang, Xiang Xie, Guolin Li, Zhihua Wang
• Abstract summary: Many low-quality endoscopic images exist due to limited illumination and complex environment in GI tract. This paper proposes a convolutional blind denoising network for endoscopic images.
• Score: 19.373025463383385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quality of images captured by wireless capsule endoscopy (WCE) is key for doctors to diagnose diseases of gastrointestinal (GI) tract. However, there exist many low-quality endoscopic images due to the limited illumination and complex environment in GI tract. After an enhancement process, the severe noise become an unacceptable problem. The noise varies with different cameras, GI tract environments and image enhancement. And the noise model is hard to be obtained. This paper proposes a convolutional blind denoising network for endoscopic images. We apply Deep Image Prior (DIP) method to reconstruct a clean image iteratively using a noisy image without a specific noise model and ground truth. Then we design a blind image quality assessment network based on MobileNet to estimate the quality of the reconstructed images. The estimated quality is used to stop the iterative operation in DIP method. The number of iterations is reduced about 36% by using transfer learning in our DIP process. Experimental results on endoscopic images and real-world noisy images demonstrate the superiority of our proposed method over the state-of-the-art methods in terms of visual quality and quantitative metrics.

Related papers

• Unsupervised Training of a Dynamic Context-Aware Deep Denoising Framework for Low-Dose Fluoroscopic Imaging [6.130738760059542]
  Fluoroscopy is critical for real-time X-ray visualization in medical imaging. Low-dose images are compromised by noise, potentially affecting diagnostic accuracy. We propose an unsupervised training framework for dynamic context-aware denoising of fluoroscopy image sequences.
  arXiv  Detail & Related papers  (2024-10-29T13:39:31Z)
• Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering [0.03262230127283451]
  Ultrasound images are low in quality and suffer from noisy annotations caused by inter-observer variations. We propose an UnSupervised UltraSound image Quality assessment Network, US2QNet, that eliminates the burden and uncertainty of manual annotations. The proposed framework achieved 78% accuracy and superior performance to state-of-the-art clustering methods.
  arXiv  Detail & Related papers  (2023-07-05T17:34:58Z)
• Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning [0.0]
  We propose a method for denoising diffusion-weighted images (DWI) of the brain using a convolutional neural network trained on realistic, synthetic MR data. We show that the application of our method allows for a significant reduction in scan time by lowering the number of repeated scans.
  arXiv  Detail & Related papers  (2022-06-01T08:14:35Z)
• Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training [73.60883490436956]
  We propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification. We employ the self-ensemble model with a noisy label filter to efficiently select the clean and noisy samples. We also design a novel global and local representation learning scheme to implicitly regularize the networks to utilize noisy samples.
  arXiv  Detail & Related papers  (2022-05-10T07:50:08Z)
• Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training [50.018580462619425]
  We propose a novel framework, namely Pixel-level Noise-aware Generative Adrial Network (PNGAN) PNGAN employs a pre-trained real denoiser to map the fake and real noisy images into a nearly noise-free solution space. For better noise fitting, we present an efficient architecture Simple Multi-versa-scale Network (SMNet) as the generator.
  arXiv  Detail & Related papers  (2022-04-06T14:09:02Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion [38.62448918459113]
  We propose a new denoising method based on score-based reverse diffusion sampling. Our network, trained only with coronal knee scans, excels even on out-of-distribution in vivo liver MRI data.
  arXiv  Detail & Related papers  (2022-03-23T10:35:06Z)
• De-Noising of Photoacoustic Microscopy Images by Deep Learning [0.9786690381850356]
  Photoacoustic microscopy (PAM) imaging suffers from noise due to the maximum permissible exposure of laser intensity, attenuation of ultrasound in the tissue, and the inherent noise of the transducer. We propose a deep learning-based method to remove complex noise from PAM images without mathematical priors and manual selection of settings for different input images.
  arXiv  Detail & Related papers  (2022-01-12T05:13:57Z)
• Improving Blind Spot Denoising for Microscopy [73.94017852757413]
  We present a novel way to improve the quality of self-supervised denoising. We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network.
  arXiv  Detail & Related papers  (2020-08-19T13:06:24Z)
• Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation [52.75909685172843]
  Real-world image noise removal is a long-standing yet very challenging task in computer vision. We propose a novel unified framework to deal with the noise removal and noise generation tasks. Our method learns the joint distribution of the clean-noisy image pairs.
  arXiv  Detail & Related papers  (2020-07-12T09:16:06Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

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.