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Photo Realistic single Image Super Resolution Using Autoencoders and TF2.0
Imagine you take a small, blurry picture and magically turn it into a crisp, high-definition photo. This is what Photo-Realistic Single Image Super-Resolution (SISR) does! Let’s dive into how to achieve this using Autoencoders and TensorFlow 2.0 (TF2.0)
What is Super-Resolution?
Super-resolution is like a superpower for images! You take a low-resolution (LR) image and make it bigger and clearer, turning it into a high-resolution (HR) image without losing quality. This technique is useful for enhancing old photos, improving medical images, or even making your favorite games look better.
What is a Neural Network?
Think of a neural network as a brain. It takes inputs (like a low-res image), processes them with its neurons (layers), and gives you the output (a high-res image). In our case, we will use Autoencoders.
Key Concepts Simplified:
1. Autoencoders:
Imagine you have a crumpled paper, and you want to flatten it. An autoencoder helps you do…