Hossain, Muhammad IqbalAhmad, IstiaqAzam, Labib SadmanBhuiyan, Moinul HossainMamun, Abdullah Al2025-01-212025-01-21©20242024-10ID 20301056ID 21301643ID 20301002ID 20301062http://hdl.handle.net/10361/25237Cataloged from PDF version of thesis.Includes bibliographical references (pages 45-48).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.In a world where visual content plays a crucial role in anything imaginable, the need for sharper, more detailed images has never been more important. This research paper explores innovative approaches to improve the quality of single images through the application of Deep Learning techniques, specifically GAN architectures. The research focuses on developing an efficient and feasible model that can be trained in a consumer-grade GPU. Among various architectures, the research focused on the RRDB model for further development. With the modification of the RRDB model and the implementation of activation functions combination and proper loss functions, this research seeks to achieve enhanced performance and effectiveness. Finally, the proposed model MSRGAN was developed which was capable of training on a consumer-grade GPU with an average amount of video RAM. The model possesses the capability for 4x upscaling. For testing the performance, the research used PSNR and SSIM evaluation metrics where the MSRGAN has outperformed the basic SRGAN and ESRGAN.56 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.Single image super resolutionSISRSuper resolution generative adversarial networksSRGANGANGenerative adversarial networksModified super resolution generative adversarial networksMSRGANESRGANMachine learning--Technological innovations.Generative programming (Computer science).Artificial intelligence--Computer programs.Image reconstruction.High resolution imaging.Graphics processing units--Programming.Thesis