Laser stripe image denoising using convolutional autoencoder

Fang, Zhuoqun; Jia, Tong; Chen, Qiusheng; Mei, Xu; Yuan, Xi; Wu, Chengdong

doi:10.1016/j.rinp.2018.08.023

Cited by 24 publications

(7 citation statements)

References 18 publications

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“…One of the exciting and practically significant areas of application of this type of neural network is processing and improving the quality of images. The variety of autoencoders includes various architectures and training methods, allowing them to effectively solve various problems, such as removing noise, background, or other unwanted elements from processed images [17][18][19][20][21][22][23][24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Visible watermarks inpainting using deep learning for multimedia application

Gapon,

Yadrets,

Voronin

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

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In recent decades, with the development of digital technologies and the spread of the Internet, photography has become an integral part of our daily lives. Often, images with objects superimposed in text, symbols, or drawings. These superimposed objects can serve a variety of purposes. Adding text or graphics can highlight specific details in an image or convey important information, such as the event's date, location, or specifics. Adding graphic elements may be used commercially to promote products, services, or a brand. Another area of application of such objects is the field of intellectual property protection or verification of the authenticity of a digital image. The problem with such images is that objects can completely overlap the image, leading to a distortion of the perceived information. In this article, we propose an effective convolutional autoencoder model for removing watermarking and restoring an image's usable part. This network can perfectly remove the embedded, visible watermark, restoring the image. Experimental results demonstrate the effectiveness and superiority of our scheme.

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Section: Related Workmentioning

confidence: 99%

Visible watermarks inpainting using deep learning for multimedia application

Gapon,

Yadrets,

Voronin

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

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“…Once the pre-schooling is over, the decoders are eliminated and a goal layer is connected to the inner most illustration layer and skilled through softmax class or logistic regression. The different use of autoencoder is in function generation [14]. The output functions from the skilled samples are used for schooling a separate classifier like a guide vector device or random choice forest.…”

Section: Backgroundsmentioning

confidence: 99%

Recognition of cancer mediating genes using the novel MLP-SDAE model

Sheet

Ghosh

et al. 2022

Preprint

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In the present article, we introduce a deep auto-encoder model to have identified some possible genes mediating different cancer. This new version is a regression-primarily based totally fateful version primarily based totally on MLP and Stacked Denoising Auto-Encoder and the model name is MLP-SDAE. This version is educated via way of means of the usage of a Stacked Denoising Auto-Encoder for characteristic selection. Also, there’s a MLP structure for backpropagation. We have also included dropout to prevent over-fitting to improve our proposed model as well as performance. The process entails companies of gene-primarily based totally correlation coefficients and in the end decided on a few feasible genes. A specified comparative examine has been executed with a number of the prevailing deep studying procedures, such as a Recurrent Neural Network (RNN), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Auto-encoder (AE), and Denoising Auto-encoder (DAE). Four microarray gene expression data sets like human leukemia, lung, colon, and breast cancer are 1 Springer Nature 2021 L A T E X template Recognition of cancer mediating genes using the novel MLP-SDAE model successfully applied in this model. The results are verified using the preliminary test, gene expression profile plots, biochemical pathway, t-test, and also identified GO type of the important genes based on p-values.

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“…A CAE (Convolutional autoencoder) is also used in Reference [16] to restore the corrupted laser stripe images of the depth sensor by denoising the data.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

García-Ordás

Benítez-Andrades

García-Rodríguez

et al. 2020

Sensors

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The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

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Laser stripe image denoising using convolutional autoencoder

Cited by 24 publications

References 18 publications

Visible watermarks inpainting using deep learning for multimedia application

Visible watermarks inpainting using deep learning for multimedia application

Recognition of cancer mediating genes using the novel MLP-SDAE model

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

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