【图像去噪】基于BdCNN算法实现图像去噪含Matlab源码

1 简介

The removal of mixed noise is a stiff problem since the distribution of the noise cannot be predicted accurately. The most common mixed noise is the combination of Additive White Gaussian Noise (AWGN) and Impulse Noise (IN). Many methods first attempt to remove IN but it might collapse the texture of the image. In this paper, we propose a new learning-based method using convolutional neural network (CNN) for removing mixed gaussian-impulse noise. Since our denoising network can remove various level of mixed noise, neither the preprocessing for removing IN nor noise-level estimation is necessary.​

2 部分代码

% Use this code when training​training_image_list = {};for a=1000:2:1200        training_image_list = [training_image_list,['image/train/train_image_gray', num2str(a) ,'.png']];    end​​patch_size = 33;​train_data = zeros(patch_size, patch_size, 1000000, 'single');train_label = zeros(patch_size, patch_size, 1000000, 'single');​num_patches = 0;​% Make training datafor image_index = 1 : length(training_image_list)    fprintf('Reading %s\n', training_image_list{image_index});        for impulse_loop =1        for impulse_noise_rate = 0:5:45             for gaussian_noise_sigma = 0:10:50​                        img_original = im2single(imread(training_image_list{image_index}));            img_original = padarray(img_original, ceil(size(img_original)/patch_size)*patch_size-size(img_original) ,'symmetric','post');                        % AWGN            img_noisy = img_original + (gaussian_noise_sigma / 255) * randn(size(img_original));                        % RVIN            img_noise_position = rand(size(img_original)) < impulse_noise_rate / 100;            img_noisy(repmat(img_noise_position,1,1)) = rand(1, sum(img_noise_position(:)));                        if impulse_noise_rate>0                if gaussian_noise_sigma >0                    if rand<0.15                        % SPIN                        img_noise_position = rand(size(img_original)) < randi(30) / 100;                        img_noisy(repmat(img_noise_position,1,1)) = rand(1, sum(img_noise_position(:)))>0.5;                    end                end            end                                    tmp_data = im2col(img_noisy, [patch_size, patch_size], 'distinct');            tmp_data = reshape(tmp_data, patch_size, patch_size, []);            tmp_label = im2col(img_original, [patch_size, patch_size], 'distinct');            tmp_label = reshape(tmp_label, patch_size, patch_size, []);                        train_data(:, :, num_patches + 1 : num_patches + size(tmp_data, 3)) = tmp_data;            train_label(:, :, num_patches + 1 : num_patches + size(tmp_label, 3)) = tmp_label;                        num_patches = num_patches + size(tmp_data, 3);            end        end    endend​train_data = train_data(:, :, 1 : num_patches);train_label = train_label(:, :, 1 : num_patches);​​% reshape to MxNx1xCtrain_data = reshape(train_data, patch_size, patch_size, 1, []);train_label = reshape(train_label, patch_size, patch_size, 1, []);​% save('mixed_data.mat','train_data','train_label');% clear allfprintf('Complete.\n');

3 仿真结果

4 参考文献

Ryo Abiko, and Masaaki Ikehara. "Blind Denoising of Mixed Gaussian-impulse Noise by Single CNN." ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.

博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。

部分理论引用网络文献，若有侵权联系博主删除。