Image processing based on Neural networks.
BM3D-Net: A Convolutional Neural Network For Transform-Domain Collaborative Filtering
Obtaining Enriched Image Qualities in image processing is an essential after denoising.
Unfold BM3D algorithm into a convolutional neural network structure, with “extraction” and “aggregation” layers to model block matching stage in BM3D.
The network is applied to three denoising tasks: 1-gray-scale image denoising. 2-color image denoising. 3-depth map denoising.
Image denoising is a preprocessing step in image analysis.
image denoising roughly categorized into Traditional method and Learning based methods.
Traditional methods:
spatial filtering method
wavelet transformation based method
shrinkage function approach.
Learning based methods:
Natural image prior based method
Dictionary learning
Space coding
Deep learning
Network extends the computational procedures of BM3D to learnable CNN. BM3D-Net 5 layers Extraction layer Convolution layer Non-linear transform layer Convolution layer Aggregation layer
procedure to run the software
open matlab 2016a or higher version open the project directory run the file-to-run.m file to begin the .m file automatically loads the required files and directories Continue further by selecting the options training testing comparing and final results.
In case of common errors try changing the local drive to F:/ or replace the local drive letter in the following files
- main_switch_UI.m
- train_d.m
- training_d.m