A color vision approach for reconstructing color images in different lighting conditions based on auto encoder technique using deep neural networks
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We propose a color vision approach that enables normalizing images based on autoencoder technique using deep neural networks. The proposed model consists of three main different steps: image processing, encoding and decoding. In the image processing part, an efficient image processing method is used to resize acquired images into a finite image resolution equal to the number of input nodes of an autoencoder. Autoencoder comprises encoding and decoding processes. Secondly, the encoding process based on deep neural networks generates a code of an input image and finally the decoding process using deep neural networks reconstructs the original image from the code generated by the encoder. The autoencoder is trained with more than ten thousand resized image dataset using convolutional neural networks. The experimental results verified that the proposed model enables reconstructing predefined normalized images from original images which can be used in sophisticated color vision applications.