Upscaling Images Resolution Using Generative Adversarial Networks

This research paper explores the application of Generative Adversarial Networks (GANs) for enhancing image resolution through upscaling techniques. GANs use adversarial training to create high-resolution images from low-resolution inputs. The GAN network has generator to produce images and discriminator to maximize its accuracy in distinguishing between the two types of images fake and real. This paper uses a U-Net architecture for the generator, which combines upsampling and downsampling blocks with skip connections to restore dimensions. The discriminator performs downsampling which uses convolutional layers, batch normalization, and LeakyReLU activation. It is implemented as a PatchGAN which evaluates picture realism as a patch. The generator-discriminator interaction is controlled by a complicated loss function that combines L1 loss for structural similarity and GAN loss for adversarial training. This work clarifies the complex dynamics and effectiveness of GANs in upscaling image resolution through extensive testing, offering insightful information about the training approaches and architectural elements that promote performance gains.