Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian Noise - 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 (Entropy, 2023)
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 (DVA) 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.