{"title":"White Noise Suppression Based on Wiener Filtering Using Neural Network Technologies in the Domain of the Discrete Wavelet Transform","authors":"K. A. Alimagadov, S. V. Umnyashkin","doi":"10.1134/s106373972307003x","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">\n<b>Abstract</b>—</h3><p>Computer vision algorithms are widely used in solving a number of applied problems. The correct operation of such algorithms depends on the photo and video data that they receive at the input, which are subject to the effect of noise; hence, noise suppression is an important stage in low-level digital image processing. In this work, the Wiener filtering of normal white noise with using neural networks in the domain of the discrete wavelet transform is studied. The architecture of the networks and the algorithm developed for their application for filtering in the domain of a discrete wavelet transform are described. The proposed algorithm is tested on the BSDS500 dataset at various noise levels. The filtering quality is evaluated by the calculated signal-to-noise ratio (SNR) and structural similarity index (SSIM) values. The results of processing test images indicate that the developed algorithm is superior in noise reduction quality to most of the other considered filters, including Wiener filtering without the use of neural networks in the domain of the discrete wavelet transform.</p>","PeriodicalId":21534,"journal":{"name":"Russian Microelectronics","volume":"26 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Microelectronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s106373972307003x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract—
Computer vision algorithms are widely used in solving a number of applied problems. The correct operation of such algorithms depends on the photo and video data that they receive at the input, which are subject to the effect of noise; hence, noise suppression is an important stage in low-level digital image processing. In this work, the Wiener filtering of normal white noise with using neural networks in the domain of the discrete wavelet transform is studied. The architecture of the networks and the algorithm developed for their application for filtering in the domain of a discrete wavelet transform are described. The proposed algorithm is tested on the BSDS500 dataset at various noise levels. The filtering quality is evaluated by the calculated signal-to-noise ratio (SNR) and structural similarity index (SSIM) values. The results of processing test images indicate that the developed algorithm is superior in noise reduction quality to most of the other considered filters, including Wiener filtering without the use of neural networks in the domain of the discrete wavelet transform.
期刊介绍：
Russian Microelectronics covers physical, technological, and some VLSI and ULSI circuit-technical aspects of microelectronics and nanoelectronics; it informs the reader of new trends in submicron optical, x-ray, electron, and ion-beam lithography technology; dry processing techniques, etching, doping; and deposition and planarization technology. Significant space is devoted to problems arising in the application of proton, electron, and ion beams, plasma, etc. Consideration is given to new equipment, including cluster tools and control in situ and submicron CMOS, bipolar, and BICMOS technologies. The journal publishes papers addressing problems of molecular beam epitaxy and related processes; heterojunction devices and integrated circuits; the technology and devices of nanoelectronics; and the fabrication of nanometer scale devices, including new device structures, quantum-effect devices, and superconducting devices. The reader will find papers containing news of the diagnostics of surfaces and microelectronic structures, the modeling of technological processes and devices in micro- and nanoelectronics, including nanotransistors, and solid state qubits.
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