Robbani, Mohammad ElhamHossain, AdilUl Haque Sazid, Md. RiazSiam, Sk. ShahiduzzamanAbtahee, WasiuChakrabarty A.2026-08-122026-08-122021-01-01M. E. Robbani, A. Hossain, M. R. U. H. Sazid, S. S. Siam, W. Abtahee and A. Chakrabarty, "Enhancing Object Clarity In Single Channel Night Vision Images Using Deep Reinforcement Learning," 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Brisbane, Australia, 2021, pp. 1-6, doi: 10.1109/CSDE53843.2021.9718444.97816654955232-s2.0-85127836647https://hdl.handle.net/10361/28989This paper implements a system of enhancing single channel night vision images using reinforcement learning approach and optimizing pixel prediction using q-table. We implemented some models to learn and process a small static images dataset using a reward bias q-table in a reinforcement learning architecture thus optimizing computational complexities and requirements of large dataset with the help of q-table. It also outperformed with respect to existing CNN models like SRCNN. Where SRCNN is observed to generate a PSNR of 24.813 on average at 256 batch size. Our system generated a PSNR of 24.1 on average with results in a 10.29% increase of relative efficiency at 3000 epoch. It has shown a 10.39% and 10.36% increase of efficiency with respect to VDSR (at 128 batch size) model and DRCN (at filter number 16) model respectively.6 Pagesen-USAIData-set limitationsDeep Q learningDeep Q networkIntelligent agentNight footageReinforcement learningSingle channel imagesNight vision.Image processing--Digital techniques.Conference Proceeding10.1109/CSDE53843.2021.9718444