High-Fidelity Image Upscaling via Convolutional Neural Networks

Abstract

Abstract—This project uses Convolutional Neural Networks (CNNs) to improve image quality by making blurry or noisy images clearer. It focuses on tasks like sharpening images, removing noise, and fixing distortions. Using models like SRCNN and U-Net, the system learns how to turn low-quality images into high-quality ones. The results show that CNNs work better than traditional methods for enhancing images.This work studies how Convolutional Neural Networks (CNNs) effectively improve image quality in a large variety of tasks including noise reduction, deblurring, and detail restoration. Utilizing and training models such as SRCNN and U-Net, this project assesses the performance on benchmark datasets. The results demonstrate that CNN has higher potential in low level complex transformation learning for high quality image restoration than conventional image processing approaches. Keywords:super-resolution, feature extraction, residual learning, and evaluation metrics like PSNR and SSIM.