DnCNN
DnCNN, short for Denoising Convolutional Neural Network, is a deep learning model introduced in 2017 by Kai Zhang, Wangmeng Zuo, and Lei Zhang for image denoising. It focuses on removing additive white Gaussian noise from images and demonstrates the ability to handle varying noise levels with a single trained model by learning from a mixture of noise intensities.
The architecture centers on a 20-layer convolutional neural network composed of 3x3 filters and 64 feature
Training typically uses pairs of clean and synthetically Noised images with Gaussian noise across a range