Skip to content
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising (TIP, 2018)
Branch: master
Clone or download
Latest commit 0452eb1 Dec 13, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
TrainingCodes/FFDNet_TrainingCodes_v1.0
figs
models
testsets Init Oct 11, 2017
utilities generate multivariate Gaussian noise May 21, 2018
Demo_AWGN_Color.m
Demo_AWGN_Color_Clip.m
Demo_AWGN_Gray.m
Demo_AWGN_Gray_Clip.m
Demo_AWGN_spatially_variant_noise.m
Demo_REAL_Color.m
Demo_REAL_Gray.m
Demo_multivariate_Gaussian_noise.m Add files via upload May 21, 2018
README.md Update README.md Dec 13, 2018

README.md

FFDNet

FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising

New Training and Testing Codes (PyTorch)

FFDNet-pytorch

An Analysis and Implementation of the FFDNet Image Denoising Method

Abstract

Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled subimages,achieving a good trade-off between inference speed and denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including

  • the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network,
  • the ability to remove spatially variant noise by specifying a non-uniform noise level map, and
  • faster speed than benchmark BM3D even on CPU without sacrificing denoising performance.

Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.

Network Architecture

architecture The input image is reshaped to four sub-images, which are then input to the CNN together with a noise level map. The final output is reconstructed by the four denoised sub-images

Test FFDNet Models

  • Demo_AWGN_Gray.m is the testing demo of FFDNet for denoising grayscale images corrupted by AWGN.

  • Demo_AWGN_Color.m is the testing demo of FFDNet for denoising color images corrupted by AWGN.

  • Demo_AWGN_Gray_Clip.m is the testing demo of FFDNet for denoising grayscale images corrupted by AWGN with clipping setting.

  • Demo_AWGN_Color_Clip.m is the testing demo of FFDNet for denoising color images corrupted by AWGN with clipping setting.

  • Demo_REAL_Gray.m is the testing demo of FFDNet for denoising real noisy (grayscale) images.

  • Demo_REAL_Color.m is the testing demo of FFDNet for denoising real noisy (color) images.

  • Demo_multivariate_Gaussian_noise.m is the testing demo of FFDNet for denoising noisy images corrupted by multivariate (3D) Gaussian noise model N([0,0,0]; Sigma) with zero mean and covariance matrix Sigma in the RGB color space.

Results on Real Noisy Images from The Darmstadt Noise Dataset

PSNR: 37.61dB

The left is the noisy image from The Darmstadt Noise Dataset. The right is the denoised image by FFDNet+.

Image Denoising for AWGN

Grayscale Image Denoising

Color Image Denoising

The left is the noisy image corrupted by AWGN with noise level 75. The right is the denoised image by FFDNet.

Real Image Denoising

The left is the real noisy image. The right is the denoised image by FFDNet.

example

Extension

  • Demo_multivariate_Gaussian_noise.m is the testing demo of FFDNet for denoising noisy images corrupted by multivariate (3D) Gaussian noise model N([0,0,0]; Sigma) with zero mean and covariance matrix Sigma in the RGB color space.

Requirements and Dependencies

To run the code, you should install Matconvnet first. Alternatively, you can use function vl_ffdnet_matlab to perform denoising without Matconvnet.

Citation

@article{zhang2018ffdnet,
  title={FFDNet: Toward a Fast and Flexible Solution for {CNN} based Image Denoising},
  author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
  journal={IEEE Transactions on Image Processing},
  year={2018},
}
You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.