FFDNet
FFDNet is a convolutional neural network designed for image denoising that emphasizes speed and flexibility. It introduces a conditioning mechanism by incorporating a noise level map as part of the input, enabling a single model to handle multiple noise levels and both blind and non-blind denoising scenarios without retraining.
Input and architecture: The method takes a noisy image and a corresponding noise level map, concatenating them
Noise modeling and versatility: The noise level map provides per-pixel information about the standard deviation of
Training and performance: FFDNet is trained on pairs of clean and synthetically corrupted images, typically using
Impact and scope: The approach has influenced subsequent conditioning-based denoising models and extensions to related restoration