Alam, Md. AshrafulFaisal, AsmAhmad, AshhabTazwar, Asif2024-08-292024-08-2920222022-01ID 16201049ID 17301162ID 21301732http://hdl.handle.net/10361/23943Cataloged from PDF version of thesis.Includes bibliographical references (pages 67-69).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.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.69 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.AutoencoderImage reconstructionDeep neural networksColor visionNeural networks (Computer science)Thesis