Download
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
Project Samples
Categories
Computer Vision LibrariesFollow DnCNN
Other Useful Business Software
ACI Learning: Internal Audit, Cybersecurity, and IT Training
Traditional training doesn't equip employees with the practical skills they need to drive business success. ACI Learning provides hands-on IT and cybersecurity training designed to build real-world, on-the-job skills. Our outcome-based programs empower employees with certification prep, industry-recognized credentials, and flexible learning options. With expert-led video training, labs, and scalable solutions, we help businesses, individuals, governments, and academic institutions develop a skilled workforce, align with business goals, and stay ahead in a rapidly evolving digital world.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of DnCNN!
