Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image

Quan, Yuhui; Chen, Mingqin; Pang, Tongyao; Gao, Hui

doi:10.1109/cvpr42600.2020.00196

Cited by 287 publications

(210 citation statements)

References 20 publications

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“…On idea possibly overcoming this hurdle is called "image-blind denoising" proposed by 27,28 , in which they viewed the noisy image or void image as the reference image to denoise. Moreover, Chen, J. et al 29 tried to extract the noise distribution from the noisy image and gain denoised images through removing the noise for noisy data; Quan, Y. et al 30 augmented the data by Bernoulli sampling and denoise image with dropout. Besides, Bepler, T. et al 31,32 applied noise2noise into Cryo-EM image denoising.…”

Section: Discussionmentioning

confidence: 99%

Robust Denoising of Cryo-EM Images via β-GAN

Unarta

Huang

et al. 2021

Preprint

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The cryo-electron microscopy (Cryo-EM) becomes popular for macromolecular structure determination. However, the 2D images which Cryo-EM detects are of high noise and often mixed with multiple heterogeneous conformations and contamination, imposing a challenge for denoising. Traditional image denoising methods and simple Denoising Autoencoder can not remove Cryo-EM image noise well when the signal-noise-ratio (SNR) of images is meager and contamination distribution is complex. Thus it is desired to develop new effective denoising techniques to facilitate further research such as 3D reconstruction, 2D conformation classification, and so on. In this paper, we approach the robust denoising problem for Cryo-EM images by introducing a family of Generative Adversarial Networks (GAN), called β-GAN, which is able to achieve robust estimate of certain distributional parameters under Huber contamination model with statistical optimality. To address the challenge of robust denoising where the traditional image generative model might be contaminated by a small portion of unknown outliers, β-GANs are exploited to enhance the robustness of denoising Autoencoder. The method is evaluated by both a simulated dataset on the Thermus aquaticus RNA Polymerase (RNAP) and a real dataset on the Plasmodium falciparum 80S ribosome dataset (EMPIRE-10028), in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and 3D Reconstruction as well. The results show that equipped with some designs of β-GANs and the robust ℓ1-Autoencoder, one can stabilize the training of GANs and achieve the state-of-the-art performance of robust denoising with low SNR data and against possible information contamination. Our proposed methodology thus provides an effective tool for robust denoising of Cryo-EM 2D images, which is helpful for 3D structure reconstruction.

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Section: Discussionmentioning

confidence: 99%

Robust Denoising of Cryo-EM Images via β-GAN

Unarta

Huang

et al. 2021

Preprint

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“…The fundamental reason is mainly through the calculation of any non-convex prior with a large amount of calculation, which leads to complex optimization algorithms. What's more, the parametric assumptions on blur kernels could greatly improve the robustness of blind image deblurring [19,20], a principled algorithm within the maximum a posterior framework to tackle image restoration with a partially known or inaccurate degradation model [21], and other deep learning and other methods that contain a large number of real image sets and training sets using self-supervised methods [22][23][24][25][26], the characteristics of training data sets are often It will greatly affect the performance of the entire model, and in many cases, the construction and processing of data sets are often very expensive and low feasibility.…”

Section: Image Deblurringmentioning

confidence: 99%

Fast Blind Deblurring of QR Code Images Based on Adaptive Scale Control

et al. 2021

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With the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which mainly achieves the effect of adaptive scale control by introducing an evaluation mechanism. Its main purpose is to solve the out-of-focus caused by lens shake, inaccurate focus, and optical noise by speeding up the latent image estimation in the process of multi-scale division iterative deblurring. The algorithm optimizes productivity under the guidance of collaborative computing, based on the characteristics of the QR codes, such as the features of gradient and strength. In the evaluation step, the Tenengrad method is used to evaluate the image quality, and the evaluation value is compared with the empirical value obtained from the experimental data. Combining with the error correction capability, the recognizable QR codes will be output. In addition, we introduced a scale control parameter to study the relationship between the recognition rate and restoration time. Theoretical analysis and experimental results show that the proposed algorithm has high recovery efficiency and well recovery effect, can be effectively applied in industrial applications.

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“…For training denoisers, the lack of clean image data to be used as groundtruth may be a challenge for certain applications like medical imaging and remote sensing [22,35,47,50]. In this scenario, different training strategies were proposed.…”

Section: Full Research Papermentioning

confidence: 99%

A Denoising Convolutional Neural Network for Self-Supervised Rank Effectiveness Estimation on Image Retrieval

Valem

Pedronette

2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

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Image and multimedia retrieval has established as a prominent task in an increasingly digital and visual world. Mainly supported by decades of development on hand-crafted features and the success of deep learning techniques, various different feature extraction and retrieval approaches are currently available. However, the frequent requirements for large training sets still remain as a fundamental bottleneck, especially in real-world and large-scale scenarios. In the scarcity or absence of labeled data, choosing what retrieval approach to use became a central challenge. A promising strategy consists in to estimate the effectiveness of ranked lists without requiring any groundtruth data. Most of the existing measures exploit statistical analysis of the ranked lists and measure the reciprocity among lists of images in the top positions. This work innovates by proposing a new and self-supervised method for this task, the Deep Rank Noise Estimator (DRNE). An algorithm is presented for generating synthetic ranked list data, which is modeled as images and provided for training a Convolutional Neural Network that we propose for effectiveness estimation. The proposed model is a variant of the DnCNN (Denoiser CNN), which intends to interpret the incorrectness of a ranked list as noise, which is learned by the network. Our approach was evaluated on 5 public image datasets and different tasks, including general image retrieval and person re-ID. We also exploited and evaluated the complementary between the proposed approach and related rank-based approaches through fusion strategies. The experimental results showed that the proposed method is capable of achieving up to 0.88 of Pearson correlation with MAP measure in general retrieval scenarios and 0.74 in person re-ID scenarios.

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Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image

Cited by 287 publications

References 20 publications

Robust Denoising of Cryo-EM Images via β-GAN

Robust Denoising of Cryo-EM Images via β-GAN

Fast Blind Deblurring of QR Code Images Based on Adaptive Scale Control

A Denoising Convolutional Neural Network for Self-Supervised Rank Effectiveness Estimation on Image Retrieval

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