Denoising Autoencoder and Contrast Enhancement for RGB and GS Images with Gaussian Noise
Robust image processing systems require input images that closely resemble real-world scenes. However, external factors, such as adverse environmental conditions or errors in data transmission, can alter the captured image, leading to information loss. These factors may include poor lighting conditi...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/10/1621 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850127061795995648 |
|---|---|
| author | Armando Adrián Miranda-González Alberto Jorge Rosales-Silva Dante Mújica-Vargas Edwards Ernesto Sánchez-Ramírez Juan Pablo Francisco Posadas-Durán Dilan Uriostegui-Hernandez Erick Velázquez-Lozada Francisco Javier Gallegos-Funes |
| author_facet | Armando Adrián Miranda-González Alberto Jorge Rosales-Silva Dante Mújica-Vargas Edwards Ernesto Sánchez-Ramírez Juan Pablo Francisco Posadas-Durán Dilan Uriostegui-Hernandez Erick Velázquez-Lozada Francisco Javier Gallegos-Funes |
| author_sort | Armando Adrián Miranda-González |
| collection | DOAJ |
| description | Robust image processing systems require input images that closely resemble real-world scenes. However, external factors, such as adverse environmental conditions or errors in data transmission, can alter the captured image, leading to information loss. These factors may include poor lighting conditions at the time of image capture or the presence of noise, necessitating procedures to restore the data to a representation as close as possible to the real scene. This research project proposes an architecture based on an autoencoder capable of handling both poor lighting conditions and noise in digital images simultaneously, rather than processing them separately. The proposed methodology has been demonstrated to outperform competing techniques specialized in noise reduction or contrast enhancement. This is supported by both objective numerical metrics and visual evaluations using a validation set with varying lighting characteristics. The results indicate that the proposed methodology effectively restores images by improving contrast and reducing noise without requiring separate processing steps. |
| format | Article |
| id | doaj-art-6bb41a64b9174a6ba29faf5664a0a01d |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-6bb41a64b9174a6ba29faf5664a0a01d2025-08-20T02:33:47ZengMDPI AGMathematics2227-73902025-05-011310162110.3390/math13101621Denoising Autoencoder and Contrast Enhancement for RGB and GS Images with Gaussian NoiseArmando Adrián Miranda-González0Alberto Jorge Rosales-Silva1Dante Mújica-Vargas2Edwards Ernesto Sánchez-Ramírez3Juan Pablo Francisco Posadas-Durán4Dilan Uriostegui-Hernandez5Erick Velázquez-Lozada6Francisco Javier Gallegos-Funes7Escuela 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, MexicoInstituto de Investigación y Desarrollo Tecnológico de la Armada de México, Veracruz 95269, 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, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional, Mexico City 07738, MexicoRobust image processing systems require input images that closely resemble real-world scenes. However, external factors, such as adverse environmental conditions or errors in data transmission, can alter the captured image, leading to information loss. These factors may include poor lighting conditions at the time of image capture or the presence of noise, necessitating procedures to restore the data to a representation as close as possible to the real scene. This research project proposes an architecture based on an autoencoder capable of handling both poor lighting conditions and noise in digital images simultaneously, rather than processing them separately. The proposed methodology has been demonstrated to outperform competing techniques specialized in noise reduction or contrast enhancement. This is supported by both objective numerical metrics and visual evaluations using a validation set with varying lighting characteristics. The results indicate that the proposed methodology effectively restores images by improving contrast and reducing noise without requiring separate processing steps.https://www.mdpi.com/2227-7390/13/10/1621lightingnoisecontrast enhancementautoencoder |
| spellingShingle | Armando Adrián Miranda-González Alberto Jorge Rosales-Silva Dante Mújica-Vargas Edwards Ernesto Sánchez-Ramírez Juan Pablo Francisco Posadas-Durán Dilan Uriostegui-Hernandez Erick Velázquez-Lozada Francisco Javier Gallegos-Funes Denoising Autoencoder and Contrast Enhancement for RGB and GS Images with Gaussian Noise Mathematics lighting noise contrast enhancement autoencoder |
| title | Denoising Autoencoder and Contrast Enhancement for RGB and GS Images with Gaussian Noise |
| title_full | Denoising Autoencoder and Contrast Enhancement for RGB and GS Images with Gaussian Noise |
| title_fullStr | Denoising Autoencoder and Contrast Enhancement for RGB and GS Images with Gaussian Noise |
| title_full_unstemmed | Denoising Autoencoder and Contrast Enhancement for RGB and GS Images with Gaussian Noise |
| title_short | Denoising Autoencoder and Contrast Enhancement for RGB and GS Images with Gaussian Noise |
| title_sort | denoising autoencoder and contrast enhancement for rgb and gs images with gaussian noise |
| topic | lighting noise contrast enhancement autoencoder |
| url | https://www.mdpi.com/2227-7390/13/10/1621 |
| work_keys_str_mv | AT armandoadrianmirandagonzalez denoisingautoencoderandcontrastenhancementforrgbandgsimageswithgaussiannoise AT albertojorgerosalessilva denoisingautoencoderandcontrastenhancementforrgbandgsimageswithgaussiannoise AT dantemujicavargas denoisingautoencoderandcontrastenhancementforrgbandgsimageswithgaussiannoise AT edwardsernestosanchezramirez denoisingautoencoderandcontrastenhancementforrgbandgsimageswithgaussiannoise AT juanpablofranciscoposadasduran denoisingautoencoderandcontrastenhancementforrgbandgsimageswithgaussiannoise AT dilanuriosteguihernandez denoisingautoencoderandcontrastenhancementforrgbandgsimageswithgaussiannoise AT erickvelazquezlozada denoisingautoencoderandcontrastenhancementforrgbandgsimageswithgaussiannoise AT franciscojaviergallegosfunes denoisingautoencoderandcontrastenhancementforrgbandgsimageswithgaussiannoise |