RESTORING DIGITAL IMAGES: A DEEP LEARNING APPROACH TO DISTORTION REMOVAL

Scenarios like old family photos and critical medical scans are often marred by noise, blur, and scratches. Therefore, “restoring images with mixed distortions is important in applications like archival photo restoration, medical imaging, and consumer photography”. However, existing methods are specialized for single distortion types, and “simultaneously restoring both unstructured and structured distortions remains a significant challenge”. thus set out to design a comprehensive restoration pipeline, aiming to build a versatile neural model, generate paired datasets of degraded and clean images, and optimize performance for high PSNR and SSIM. Using PyTorch, a PretrainedEncoderAutoencoder was built: a ResNet-18 encoder (ImageNet-pretrained) with a deconvolutional decoder. It was trained end-to-end on batches of synthetically and naturally distorted images using mean-squared-error loss. The trained model effectively removed distortions and restored image details, significantly reducing noise and recovering the main structure of images. Quantitative metrics improved accordingly (yielding higher PSNR of 30.2db and SSIM of 0.95). This practical solution can clean up noisy photographs, medical scans, or archival media, demonstrating that even a relatively simple autoencoder yields meaningful image-quality gains.