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Introduction Image upsampling is a technique used to increase the resolution of an image without compromising its quality. The traditional methods of upsampling are limited in their ability to produce high-quality images. However, with the advent of Generative Adversarial Networks (GANs), it has become possible to generate realistic and high-resolution images. GANs are a type of deep learning algorithm that consists of two neural networks – a generator and a discriminator. The generator creates new images, while the discriminator evaluates the authenticity of the generated images. This process continues until the generator produces images that are indistinguishable from real images.

In the case of image upsampling using GAN, the generator network takes a low-resolution image as input and produces a high-resolution image as output. The discriminator network then evaluates the authenticity of the generated image by comparing it to real high-resolution images. The generator network is trained to improve its output based on the feedback from the discriminator network. Over time, the generator becomes better at producing high-quality images that are almost indistinguishable from real high-resolution images. This process is known as adversarial training, and it is what makes GANs so powerful for image upsampling. A Generative Adversarial Network (GAN) Super Resolution architecture is a particular variety of GAN created to improve the resolution of low-quality photographs. A discriminator network and a generator network make up the architecture's two primary parts. A low-resolution image is sent into the generator network, which creates a high-resolution image. It generally consists of many convolutional layers and up samples the picture using a method like reversed convolution or sub- pixel convolution. The produced high-resolution photos and the actual high-resolution images are separated using the discriminator network. It is trained to distinguish between false and real photos, classifying the produced images as fake. The generator network seeks to produce realistic, high-resolution pictures that are identical to the actual pictures during training. On the other hand, the discriminator network seeks to reliably identify whether the produced pictures are phony or real. Minimizing the adversarial loss among the generator and discriminator nodes is a goal of the iterative training process. The generator loss and discriminator loss, which assess how effectively the generator produces realistic pictures and the way the discriminator distinguishes between actual and created images, are combined to form the adversarial loss. The resolution of surveillance films, satellite photos, and medical imaging have all been improved with the help of the GAN Super Resolution architecture.