A color vision approach based on the autoencoder technique and deep neural networks for reconstructing color images under various lighting conditions

Abstract

We present a color vision system that utilizes deep neural net- works to normalize pictures using the autoencoder algorithm. Image processing, encoding, and decoding are the three essen- tial processes in the proposed paradigm. An effective image processing approach is utilized to downsize acquired pictures into a finite image resolution equal to the number of input nodes of an autoencoder in the image processing section. En- coding and decoding procedures are included in the Autoen- coder. Second, a deep neural network-based encoding process creates a code for an input picture, and a deep neural network- based decoding process reconstructs the original image from the encoder’s code. Convolutional neural networks were used to train the autoencoder with over ten thousand scaled pic- ture datasets. The results of the experiments showed that the suggested model can recreate predetermined normalized pic- tures from original photographs, which may be employed in sophisticated color vision applications.