Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian Noise
Noise suppression algorithms have been used in various tasks such as computer vision, industrial inspection, and video surveillance, among others. The robust image processing systems need to be fed with images closer to a real scene; however, sometimes, due to external factors, the data that represe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/10/1467 |
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| author | Armando Adrián Miranda-González Alberto Jorge Rosales-Silva Dante Mújica-Vargas Ponciano Jorge Escamilla-Ambrosio Francisco Javier Gallegos-Funes Jean Marie Vianney-Kinani Erick Velázquez-Lozada Luis Manuel Pérez-Hernández Lucero Verónica Lozano-Vázquez |
| author_facet | Armando Adrián Miranda-González Alberto Jorge Rosales-Silva Dante Mújica-Vargas Ponciano Jorge Escamilla-Ambrosio Francisco Javier Gallegos-Funes Jean Marie Vianney-Kinani Erick Velázquez-Lozada Luis Manuel Pérez-Hernández Lucero Verónica Lozano-Vázquez |
| author_sort | Armando Adrián Miranda-González |
| collection | DOAJ |
| description | Noise suppression algorithms have been used in various tasks such as computer vision, industrial inspection, and video surveillance, among others. The robust image processing systems need to be fed with images closer to a real scene; however, sometimes, due to external factors, the data that represent the image captured are altered, which is translated into a loss of information. In this way, there are required procedures to recover data information closest to the real scene. This research project proposes a Denoising Vanilla Autoencoding (<i>DVA</i>) architecture by means of unsupervised neural networks for Gaussian denoising in color and grayscale images. The methodology improves other state-of-the-art architectures by means of objective numerical results. Additionally, a validation set and a high-resolution noisy image set are used, which reveal that our proposal outperforms other types of neural networks responsible for suppressing noise in images. |
| first_indexed | 2024-03-10T21:17:09Z |
| format | Article |
| id | doaj.art-f8ac472021d64202a7e51077f13a6b85 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T21:17:09Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-f8ac472021d64202a7e51077f13a6b852023-11-19T16:25:20ZengMDPI AGEntropy1099-43002023-10-012510146710.3390/e25101467Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian NoiseArmando Adrián Miranda-González0Alberto Jorge Rosales-Silva1Dante Mújica-Vargas2Ponciano Jorge Escamilla-Ambrosio3Francisco Javier Gallegos-Funes4Jean Marie Vianney-Kinani5Erick Velázquez-Lozada6Luis Manuel Pérez-Hernández7Lucero Verónica Lozano-Vázquez8Escuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional, Mexico City 07738, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional, Mexico City 07738, MexicoDepartamento de Ciencias Computacionales, Tecnológico Nacional de México, Cuernavaca 62490, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07738, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional, Mexico City 07738, MexicoDepartamento de Ciencias Computacionales, Tecnológico Nacional de México, Cuernavaca 62490, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional, Mexico City 07738, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional, Mexico City 07738, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional, Mexico City 07738, MexicoNoise suppression algorithms have been used in various tasks such as computer vision, industrial inspection, and video surveillance, among others. The robust image processing systems need to be fed with images closer to a real scene; however, sometimes, due to external factors, the data that represent the image captured are altered, which is translated into a loss of information. In this way, there are required procedures to recover data information closest to the real scene. This research project proposes a Denoising Vanilla Autoencoding (<i>DVA</i>) architecture by means of unsupervised neural networks for Gaussian denoising in color and grayscale images. The methodology improves other state-of-the-art architectures by means of objective numerical results. Additionally, a validation set and a high-resolution noisy image set are used, which reveal that our proposal outperforms other types of neural networks responsible for suppressing noise in images.https://www.mdpi.com/1099-4300/25/10/1467denoising vanilla autoencoderimagesnoise |
| spellingShingle | Armando Adrián Miranda-González Alberto Jorge Rosales-Silva Dante Mújica-Vargas Ponciano Jorge Escamilla-Ambrosio Francisco Javier Gallegos-Funes Jean Marie Vianney-Kinani Erick Velázquez-Lozada Luis Manuel Pérez-Hernández Lucero Verónica Lozano-Vázquez Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian Noise Entropy denoising vanilla autoencoder images noise |
| title | Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian Noise |
| title_full | Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian Noise |
| title_fullStr | Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian Noise |
| title_full_unstemmed | Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian Noise |
| title_short | Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian Noise |
| title_sort | denoising vanilla autoencoder for rgb and gs images with gaussian noise |
| topic | denoising vanilla autoencoder images noise |
| url | https://www.mdpi.com/1099-4300/25/10/1467 |
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