Image Resolution Enhancing Using ESRGAN Models.

Author Reg. ID: TIJER_151507

Published Paper Id: TIJER2403118

Published In: Volume 11 Issue 3, March-2024

Abstract: Image Enhancing System Using ESRGAN Model represents the increasing demand for high-quality video content in fields like medical imaging, satellite imaging, scientific research, etc. There is a growing need for solutions to enhance the quality of the existing video footage and images. Our project aims to do so using Deep learning algorithms on Enhanced Super Resolution Generative Adversarial Networks (ESRGAN) to upscale the resolution of images. This project focuses on enhancing the visual quality of images by upscaling them while preserving details and minimizing artifacts. The primary goal is to develop innovative algorithms and methods for increasing the resolution of images such as Conv2DTranspose, Cropping2D and customize loss functions. The ESRGAN model is a generative adversarial network that consists of two parts: a generator network that produces high-resolution images and a discriminator network that evaluates the generated images. The generator and discriminator are trained in a competition to produce high quality images that are indistinguishable from real images.

Keywords: Generative Adversarial Networks (GANs), Enhanced Super Resolution Generative Adversarial Networks (ESRGAN), Convolutional neural networks (CNNs), artificial neural networks (ANNs),deep neural networks (DNNs), Peak signal-to-noise ratio (PSNR).

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Page No: a878-a881

Country: Thane, MAHARASHTRA, India

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2403118

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2403118

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