Game engines such as Unreal Engine 5 are widely used to create photo-realistic renderings. To run these renderings at high quality without experiencing any performance issues,high-performance hardware is often required. In situations where the hardware is lacking,users may be forced to lower the quality and resolution of renderings to maintain goodperformance. While this may be acceptable in some situations, it limits the benefit that apowerful tool like Unreal Engine 5 can provide. This thesis aims to explore the possibilityof using a Real-ESRGAN, fine-tuned on a custom data set, to increase both the resolutionand quality of screenshots taken in Unreal Engine 5. By doing this, users can lower theresolution and quality of their Unreal Engine 5 rendering while still being able to generatehigh quality screenshots similar to those produced when running the rendering at higherresolution and higher quality. To accomplish this, a custom data set was created by randomizing camera positionsand capturing screenshots in an Unreal Engine 5 rendering. This data set was used to finetune a pre-trained Real-ESRGAN model. The fine-tuned model could then generate imagesfrom low resolution and low quality screenshots taken in Unreal Engine 5. The resultingimages were analyzed and evaluated using both quantitative and qualitative methods.The conclusions drawn from this thesis indicate that images generated using the finetuned weights are of high quality. This conclusion is supported by quantitative measurements, demonstrating that the generated images and the ground truth images are similar.Furthermore, visual inspection conducted by the authors confirms that the generated images are similar to the reference images, despite occasional artifacts.

This thesis investigates the impact of image enhancement techniques on object detection for carsin real-world traffic scenarios. The study focuses on upscaling and light correction treatments andtheir effects on detecting cars in challenging conditions. Initially, a YOLOv8x model is trained on clear static car images. The model is then evaluated on a test dataset captured in real-world driving with images from a front-mounted camera on a car, incorporating various lighting conditions and challenges. The images are then enhanced with said treatments and then evaluated again. The results in this experiment with its specific context show that upscaling seems to decreasemAP performance while lighting correction slightly improves accuracy. Additional training on acomplex image dataset outperforms all other approaches, highlighting the importance of diverse and realistic training data. These findings contribute to advancing computer vision research for object detection models.