Image Super-Resolution and Upscaling

AI-powered super-resolution enhances low-resolution images by predicting missing pixel details. Deep learning models like ESRGAN and Real-ESRGAN reconstruct high-fidelity visuals from compressed or pixelated images.

Super-resolution AI is widely used in gaming, medical imaging, and film restoration. The technology employs convolutional layers that extract features from existing pixels and generate high-frequency details to improve sharpness.

GAN-based super-resolution networks utilize adversarial training, where a discriminator evaluates upscaled images, ensuring photorealistic quality.