Generative Adversarial Networks and Super Resolution: A Machine Learning Approach

Price: $195.00
(as of Dec 24,2024 21:25:30 UTC – Details)

Publisher ‏ : ‎ Wiley-Scrivener; 1st edition (August 5, 2025)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 300 pages
ISBN-10 ‏ : ‎ 1119842182
ISBN-13 ‏ : ‎ 978-1119842187
Item Weight ‏ : ‎ 1.74 pounds

Generative Adversarial Networks and Super Resolution: A Machine Learning Approach

Generative Adversarial Networks (GANs) have gained significant attention in the field of machine learning for their ability to generate realistic images and data that are indistinguishable from real samples. One of the applications of GANs is in the field of super resolution, where they can be used to enhance the resolution of images beyond their original quality.

Super resolution is a technique used to increase the resolution of an image, resulting in a sharper and more detailed output. This can be particularly useful in applications such as medical imaging, satellite imagery, and surveillance systems where high-resolution images are essential.

In a typical GAN setup for super resolution, the generator network takes a low-resolution image as input and generates a high-resolution image as output. The discriminator network then tries to distinguish between the generated high-resolution image and real high-resolution images. Through this adversarial training process, the generator learns to generate high-quality images that are visually indistinguishable from real ones.

Several research studies have shown the effectiveness of GANs in super resolution tasks, achieving significant improvements in image quality and sharpness. By leveraging the power of GANs, researchers and developers can create advanced super resolution models that can produce stunning high-resolution images.

In conclusion, Generative Adversarial Networks offer a promising approach to super resolution, enabling the generation of high-quality images with enhanced resolution. With further research and development, GANs can revolutionize the field of image processing and computer vision, opening up new possibilities for applications across various industries.
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