This is the Linux app named DnCNN whose latest release can be downloaded as DnCNNsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
DnCNN
DESCRIPTION:
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.
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
Programming Language
MATLAB
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/dncnn.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.