Neural Networks for High-Resolution Image Processing in Computer Vision

Present day innovative technologies like object identification, image division and photo creation are based different values of high-resolution picture processing. In the last few years, with both accuracy and time complexities the high-resolution image has been interpreted using Deep Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Generative Adversarial Networks (GANs). Highquality images that give less distortion to important areas of the image is facilitated through Super Resolution that is an ESRGAN with Enhanced Super Resolution GAN and Swin Transformer architectures. In equal manner, generation and restoration techniques of the previous generation have been left far behind by models like Diffusion Models and other in terms of plausibility and correct spatial coherency to the extent that Diffusion Models present new standards of ultra-high-quality generation & restoration. However, there are tools such as PyTorch and TensorFlow, which help in developing as well as extending complex models in both training and deployment environments and there is OpenCV which makes it possible to be working on it. This research discusses and analyse major advancements in high resolution image processing industry, review existing high resolution image processing algorithms, and forecast future trends on multimodal learning and foundation models.