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This repository implements DnCNN (“Deep CNN Denoiser”) from the paper “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising”. DnCNN is a feedforward convolutional neural network that learns to predict the residual noise (i.e. noise map) from a noisy input image, which is then subtracted to yield a clean image. This formulation allows efficient denoising, supports blind Gaussian noise (i.e. unknown noise levels), and can be extended to related tasks like image super-resolution or JPEG deblocking in some variants. The repository includes training code (using MatConvNet / MATLAB), demo scripts, pretrained models, and evaluation routines. Single model handling multiple noise levels.
Features
- Residual learning (predicting noise rather than clean image)
- Batch normalization to stabilize training
- Single model handling multiple noise levels (blind denoising)
- Demo / test scripts included
- Pretrained model weights for ease of use
- Extensions to super-resolution / deblocking tasks
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