Facial Image Denoising Using Convolutional Autoencoder Network

Tun, Naing Min; Gavrilov, A. I.; Tun, Nyan Linn

doi:10.1109/icieam48468.2020.9112080

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…The parameters used during the training process of the autoencoder can be seen in Table 1. [4][5][6][7][8][9][10][11] are the used dataset, similarity metrics and parameters. This study differs from the similar studies in the literature by testing well-known traditional and deep learning-based noise removal methods from face images of well-known noises with different metrics.…”

Section: Figure 9 the Proposed Cdae Model For Image Denoisingmentioning

confidence: 99%

“…In the last decade, there have been lots of methods designed to remove or reduce noises in image processing. Even traditional noise reduction methods have been used for years, the methods which are based on autoencoder, have attracted much attention in recent 20 years [4].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, [4] used Convolutional Autoencoder Network for facial image denoising. For this purpose, they used the ORL face database.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

Darici

Erdem

2023

Gazi University Journal of Science

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Denoising is one of the most important preprocesses in image processing. Noises in images can prevent extracting some important information stored in images. Therefore, before some implementations such as image classification, segmentation, etc., image denoising is a necessity to obtain good results. The purpose of this study is to compare the deep learning techniques and traditional techniques on denoising facial images considering two different types of noise (Gaussian and Salt&Pepper). Gaussian, Median, and Mean filters have been specified as traditional methods. For deep learning methods, deep convolutional denoising autoencoders (CDAE) structured on three different optimizers have been proposed. Both accuracy metrics and computational times have been considered to evaluate the denoising performance of proposed autoencoders, and traditional methods. The utilized standard evaluation metrics are the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). It has been observed that overall, while the traditional methods gave results in shorter times in terms of computation times, the autoencoders performed better concerning the evaluation metrics. The CDAE based on the Adam optimizer has been shown the best results in terms of PSNR and SSIM metrics on removing both types of noise

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Section: Figure 9 the Proposed Cdae Model For Image Denoisingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

Darici

Erdem

2023

Gazi University Journal of Science

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“…The input is first corrupted with noise, and the model is trained to reconstruct the original input from the corrupted one. Tun et al [ 23 ] used a convolutional autoencoder for denoising of outdoor facial images. The convolutional denoising autoencoder was used for efficient denoising of medical images by Gondara [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection for Agricultural Vehicles Using Autoencoders

Mujkic

Philipsen

Moeslund

et al. 2022

Sensors

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The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly detection instead, where convolutional autoencoders are applied to identify any objects deviating from the normal pattern. Training an autoencoder network to reconstruct normal patterns in agricultural fields makes it possible to detect unknown objects by high reconstruction error. Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. Results indicate that SSAE with an area under the curve for precision/recall (PR AUC) of 0.9353 outperforms other autoencoder models and is comparable to an object detector with a PR AUC of 0.9794. Qualitative results show that SSAE is capable of detecting unknown objects, whereas the object detector is unable to do so and fails to identify known classes of objects in specific cases.

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“…The effectiveness of an image recognition system is in its ability to obtain a clean image from a poor and noisy representation of the image. Although a number of image cleaning techniques have been proposed [26], with the deep image denoising concept pioneered in 2015 [27], the CNN based sdAE has achieved very high image cleaning performance [26]. Hence, we aim to exploit this property of the sdAE to obtain a clean appliance signal image from the mains supply signal composite image.…”

Section: Introductionmentioning

confidence: 99%

Image-based mains signal disaggregation and load recognition

Matindife

Sun

Wang

2021

Complex Intell. Syst.

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The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one dimensional in nature, suffer from poor aggregate and load signal feature localization necessitating a larger training dataset spanning very long time periods and normally require signal formatting and pre-processing. We use Gramian angular summation fields to transform the power series into a reduced image dataset that contains a rich set of localized signal features. A computer vision approach allows us to capture as much information as possible, and then propose an image-based mains load recognition system with high performance. In this paper for the entire recognition system, we use convolutional neural networks that very well adapted to vision recognition. The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network. To test the proposed system, some simulations and comparisons are carried out and the results show that our easier to handle method can achieve acceptable performance.

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Facial Image Denoising Using Convolutional Autoencoder Network

Cited by 11 publications

References 10 publications

A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

Anomaly Detection for Agricultural Vehicles Using Autoencoders

Image-based mains signal disaggregation and load recognition

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