Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging

• URL: http://arxiv.org/abs/2104.02873v1
• Date: Wed, 7 Apr 2021 02:56:57 GMT
• Title: Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging
• Authors: Yuchen He, Sihong Duan, Jianxing Li, Hui Chen, Huaibin Zheng, Jianbin Liu, Shitao Zhu, Zhuo Xu
• Abstract summary: We propose a improved Ghost Imaging (GI) method based on Denoising Convolutional Neural Networks (DnCNN) Inspired by the corresponding between input (noisy image) and output (residual image) in DnCNN, we construct the mapping between speckles sequence and the corresponding noise distribution in GI through training. The same speckles sequence is employed to illuminate unknown targets, and a de-noising target image will be obtained.
• Score: 5.737427318960774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ghost imaging (GI) has been paid attention gradually because of its lens-less imaging capability, turbulence-free imaging and high detection sensitivity. However, low image quality and slow imaging speed restrict the application process of GI. In this paper, we propose a improved GI method based on Denoising Convolutional Neural Networks (DnCNN). Inspired by the corresponding between input (noisy image) and output (residual image) in DnCNN, we construct the mapping between speckles sequence and the corresponding noise distribution in GI through training. Then, the same speckles sequence is employed to illuminate unknown targets, and a de-noising target image will be obtained. The proposed method can be regarded as a general method for GI. Under two sampling rates, extensive experiments are carried out to compare with traditional GI method (basic correlation and compressed sensing) and DnCNN method on three data sets. Moreover, we set up a physical GI experiment system to verify the proposed method. The results show that the proposed method achieves promising performance.

Related papers

• Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models [3.3463490716514177]
  High frame-rate ultrasound imaging is a cutting-edge technique that enables high frame-rate imaging. One challenge associated with high frame-rate ultrasound imaging is the high noise associated with them, hindering their wider adoption. Our proposed solution aims to enhance plane wave image quality. Specifically, the method considers the distinction between low-angle and high-angle compounding plane waves as noise. In addition, our approach employs natural image segmentation masks as intensity maps for the generated images, resulting in accurate denoising for various anatomy shapes.
  arXiv  Detail & Related papers  (2024-08-20T16:31:31Z)
• Decoupled Mixup for Generalized Visual Recognition [71.13734761715472]
  We propose a novel "Decoupled-Mixup" method to train CNN models for visual recognition. Our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions to train CNN models. Experiment results show the high generalization performance of our method on testing data that are composed of unseen contexts.
  arXiv  Detail & Related papers  (2022-10-26T15:21:39Z)
• 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)
• 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)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• 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)
• DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising [22.351540738281265]
  Deep learning techniques have been introduced to improve the image quality of LDCT images through denoising. This paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images.
  arXiv  Detail & Related papers  (2021-08-24T14:37:46Z)
• 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)
• Depth image denoising using nuclear norm and learning graph model [107.51199787840066]
  Group-based image restoration methods are more effective in gathering the similarity among patches. For each patch, we find and group the most similar patches within a searching window. The proposed method is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.
  arXiv  Detail & Related papers  (2020-08-09T15:12:16Z)
• 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.