Uddin, JiaSiam, F. M. JamiusPrince, Zahidul IslamBari, Ahmed Na sul2021-05-292021-05-2920202020-04ID 16101234ID 16101172ID 16101237http://dspace.bracu.ac.bd/xmlui/handle/10361/14437Cataloged from PDF version of thesis.Includes bibliographical references (pages 42-44).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.In this paper we present a convolutional neural network model for solving the long- standing aliasing problem in the real time 3D graphics industry. Aliasing refers to the problem of having hard jagged edges in the rendered scene. These jagged edges become a distraction and on a large enough amount, creates an unpleasant viewing experience. There are quite a few techniques out there to counter this problem, namely, FXAA, NFAA, DLAA. Our neural network architecture consists of two-dimensional convolutional layers and max pooling layers for reducing the spatial dimension. We then generate the nal output from transposed convolutional layer. Our model is trained on a specialized (trained on a per application basis) and generalized (trained on a variety of dataset to work on all possible conditions) version for anti-aliasing. Based on SSIM and PSNR scores we found out that a specialized version of our model works best, both in terms of visual score and image quality metrics.44 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.Anti-aliasingFxaa, MsaaImage processingConvolutional Neural NetworkPsnrMachine learningThesis