A fast yet reliable noise level estimation algorithm using shallow CNN-based noise separator and BP network

Xu, Shaoping; Lin, Zhenyu; Zhang, Guizhen; Liu, Tingyun; Yang, Xiaohui

doi:10.1007/s11760-019-01608-z

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…As shown in Ref. [20,27], relatively shallow CNN-based noise estimation models can be used for some image processing and denoising tasks. However, the ability to develop and prepare such estimators hinges on the availability of training data.…”

Section: Tuning With Data Quality Assessment Frameworkmentioning

confidence: 99%

“…In this manuscript, we present a framework for robust automated tuning of QD devices that combines a convolutional neural network (CNN) for device state estimation with a CNN for assessing the data quality, similar to approaches for general image noise estimation [20]. Inspired by recent efforts on using physics-based data augmentation to improve training of ML models [21][22][23][24], we use synthetic noise characteristic of QD devices to train these two networks.…”

Section: Introductionmentioning

confidence: 99%

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Toward Robust Autotuning of Noisy Quantum dot Devices

et al. 2022

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The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a "gatekeeper" system, ensuring that only reliable data is processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9) % when tested on experimental data. We then validate the functionality of the data quality control module by showing the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

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Section: Tuning With Data Quality Assessment Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Robust Autotuning of Noisy Quantum dot Devices

et al. 2022

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“…According to the process of 3D medical image surface reconstruction, assuming that each layer of BP network 17 has an information unit to be processed, the nonlinear function expression of 3D medical image overlapping area is as follows:

F (y) = \frac{1}{1 + e^{- x}} .

…”

Section: Detection Of Overlapped Area Information On 3d Medical Image...mentioning

confidence: 99%

Research on 3D medical image surface reconstruction based on data mining and machine learning

Hua

Liu

Yin

et al. 2021

Int J of Intelligent Sys

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Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that

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“…Second, to solve the problem of over-or under-fitting, we adopted the previously proposed NLE module [22], which can assess the severity of the noise interference and obtain the noise level value of the noisy image to allow us to set a more reasonable adaptive termination condition. Specifically, the residual imagen i = x 0 −x i can be obtained by subtracting the i th output image of the deep generative network from the noisy image x 0 .…”

Section: Adaptive Termination Conditionmentioning

confidence: 99%

Image Denoising Using a Novel Deep Generative Network with Multiple Target Images and Adaptive Termination Condition

Chen

et al. 2021

Applied Sciences

Self Cite

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Image denoising, a classic ill-posed problem, aims to recover a latent image from a noisy measurement. Over the past few decades, a considerable number of denoising methods have been studied extensively. Among these methods, supervised deep convolutional networks have garnered increasing attention, and their superior performance is attributed to their capability to learn realistic image priors from a large amount of paired noisy and clean images. However, if the image to be denoised is significantly different from the training images, it could lead to inferior results, and the networks may even produce hallucinations by using inappropriate image priors to handle an unseen noisy image. Recently, deep image prior (DIP) was proposed, and it overcame this drawback to some extent. The structure of the DIP generator network is capable of capturing the low-level statistics of a natural image using an unsupervised method with no training images other than the image itself. Compared with a supervised denoising model, the unsupervised DIP is more flexible when processing image content that must be denoised. Nevertheless, the denoising performance of DIP is usually inferior to the current supervised learning-based methods using deep convolutional networks, and it is susceptible to the over-fitting problem. To solve these problems, we propose a novel deep generative network with multiple target images and an adaptive termination condition. Specifically, we utilized mainstream denoising methods to generate two clear target images to be used with the original noisy image, enabling better guidance during the convergence process and improving the convergence speed. Moreover, we adopted the noise level estimation (NLE) technique to set a more reasonable adaptive termination condition, which can effectively solve the problem of over-fitting. Extensive experiments demonstrated that, according to the denoising results, the proposed approach significantly outperforms the original DIP method in tests on different databases. Specifically, the average peak signal-to-noise ratio (PSNR) performance of our proposed method on four databases at different noise levels is increased by 1.90 to 4.86 dB compared to the original DIP method. Moreover, our method achieves superior performance against state-of-the-art methods in terms of popular metrics, which include the structural similarity index (SSIM) and feature similarity index measurement (FSIM). Thus, the proposed method lays a good foundation for subsequent image processing tasks, such as target detection and super-resolution.

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A fast yet reliable noise level estimation algorithm using shallow CNN-based noise separator and BP network

Cited by 10 publications

References 23 publications

Toward Robust Autotuning of Noisy Quantum dot Devices

Toward Robust Autotuning of Noisy Quantum dot Devices

Research on 3D medical image surface reconstruction based on data mining and machine learning

Image Denoising Using a Novel Deep Generative Network with Multiple Target Images and Adaptive Termination Condition

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