Denoising RGB Images Using Deep Learning - Noise2Void

Explore denoising techniques for RGB images using deep learning in this comprehensive tutorial on Noise2Void. Learn to train a denoising model without the need for clean images, making it ideal for confocal microscopy. Dive into the implementation using Python, TensorFlow, and Keras, covering topics such as data preparation, model configuration, and training. Understand the underlying assumption that signal has structure while noise does not, enabling prediction based on surrounding pixels. Follow along with provided code examples and resources, including GitHub repositories and academic papers. Gain practical knowledge applicable to various image types, including grayscale SEM and CT images, with insights into future applications for multichannel and 3D image denoising.

Introduction
GitHub
Google Collab
Installing tensorflow
Importing dependencies
Reading images
Training and validation sets
Configuration
Model name
Denoising