Medical imaging is a vital tool for physicians to diagnose and treat various illnesses. However, these images can be noisy due to factors such as radiation and hardware limitations. This is where DU-GAN, a generative adversarial network, comes in handy.
DU-GAN is a deep learning algorithm designed for LDCT denoising in medical imaging. The generator in DU-GAN produces denoised LDCT images, and two independent branches with U-Net based discriminators perform at the image and gradient domains. Th
Noise2Fast: Removing Noise from Single Images with Blind Denoising
If you've ever taken a photo in a dimly lit room or outside at night, you know how frustrating noise can be in your images. But with recent advancements in technology, removing noise from single images has become easier than ever before. Enter Noise2Fast, a model for single image blind denoising that has been making waves in the world of image processing.
What is Blind Denoising?
Before we dive into the specifics of Noise2Fas
What is Principle Components Analysis (PCA)?
Principle Components Analysis (PCA) is a technique used in machine learning to reduce the dimensionality of data. Essentially, this means that PCA simplifies complex data by identifying groups of variables that are correlated and then combining those variables into a smaller, more manageable set of new variables called principle components or latent factors that still retain most of the original information.
How Does PCA Work?
PCA works by using a