[image denoising] image denoising based on trilateral filter with matlab code

1 brief introduction

In recent years , With the development of computer vision , The problem of fog image clarity has gradually become a research hotspot in this field . At present, the research on image defogging technology can be divided into two categories [1]： Image defogging method based on atmospheric scattering physical model and image enhancement method . The image defogging method based on the physical model of atmospheric scattering is analyzed from the perspective of the specific causes of image degradation , The effect of atmospheric scattering on the imaging process is mainly considered , The mathematical model of fog image degradation process is established

model , Solve the model parameters , The original real image is restored through the inverse process of image degradation . The defogging method based on image enhancement does not consider the cause of image degradation , But selectively highlight the features of interest in the image ​ sign , Weaken unwanted features . Generally, image defogging is achieved by enhancing the contrast of the image and correcting the color of the image , Thus, the processed image is more suitable for human visual characteristics or computer recognition system . The defogging method based on image enhancement is simple 、 Effective and widely used .

2 Part of the code

clear all;clc;warning off; close all;foldnumber = 2;linenumber = 150;string = strcat('C:\Users\hehesjtu\Desktop\test\',num2str(foldnumber),'\');Files = dir(strcat(string,'*.bmp')); LengthFiles = length(Files);t = [1:256];​for i = 1:LengthFiles    string_GT = Files(i).name(end-5:end-4);     if (strcmp(string_GT,'GT'))       ygt = getline(imread(strcat(string,Files(i).name)),linenumber);       name = strcat(Files(i).name(1:end-6));       break;    endend​picture = imread(strcat(string,name,'noisy.bmp'));block = ones(5,256,3);block(:,:,1)= block(:,:,1)*255;block(:,:,2) = 0; block(:,:,3) = 0;picture(linenumber-2:linenumber+2,:,:) = block;ynoisy = getline(imread(strcat(string,name,'noisy.bmp')),linenumber);yNLM = getline(imread(strcat(string,name,'NLM.bmp')),linenumber);yBM3D = getline(imread(strcat(string,name,'BM3D.bmp')),linenumber);yDDID = getline(imread(strcat(string,name,'DDID.bmp')),linenumber);yEPLL = getline(imread(strcat(string,name,'EPLL.bmp')),linenumber);yPGPD = getline(imread(strcat(string,name,'PGPD.bmp')),linenumber);yWNNM = getline(imread(strcat(string,name,'WNNM.bmp')),linenumber);yMCWNNM = getline(imread(strcat(string,name,'MCWNNM.bmp')),linenumber);yDnCNN = getline(imread(strcat(string,name,'DnCNN.bmp')),linenumber);yFFDNet = getline(imread(strcat(string,name,'FFDNet.bmp')),linenumber);yOur = getline(imread(strcat(string,name,'Our.bmp')),linenumber);​subplot(3,4,1)imshow(picture);​subplot(3,4,2)plot(t,ygt,'k','linewidth',2);hold on;plot(t,ynoisy,'r','linewidth',1.5);axis([1 256 50 150]);legend('y_{gt}','y');​subplot(3,4,3)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yNLM,'g','linewidth',1.5);axis([1 256 50 150]);legend('y_{gt}','NLM');​subplot(3,4,4)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yBM3D,'g','linewidth',1.5);axis([1 256 80 150]);legend('y_{gt}','BM3D');​subplot(3,4,5)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yDDID,'g','linewidth',1.5);axis([1 256 80 150]);legend('y_{gt}','DDID');​subplot(3,4,6)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yEPLL,'g','linewidth',1.5);axis([1 256 80 150]);legend('y_{gt}','EPLL');​subplot(3,4,7)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yPGPD,'g','linewidth',1.5);axis([1 256 80 150]);legend('y_{gt}','PGPD');​subplot(3,4,8)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yWNNM,'g','linewidth',1.5);axis([1 256 80 150]);legend('y_{gt}','WNNM');​subplot(3,4,9)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yMCWNNM,'g','linewidth',1.5);axis([1 256 80 150]);legend('y_{gt}','MCWNNM');​subplot(3,4,10)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yDnCNN,'g','linewidth',1.5);axis([1 256 80 150]);legend('y_{gt}','DnCNN');​subplot(3,4,11)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yFFDNet,'g','linewidth',1.5);axis([1 256 80 150]);legend('y_{gt}','FFDNet');​subplot(3,4,12)plot(t,ygt,'k','linewidth',2);hold on;plot(t,yOur,'g','linewidth',1.5);axis([1 256 80 150]);legend('y_{gt}','Ours');​​​​​​​​​​

3 Simulation results

4 reference

[1] Wu Lili . Ultrasonic image denoising algorithm based on Adaptive Wavelet and trilateral filtering [D]. Zhejiang University of technology , 2016.

About bloggers ： Good at intelligent optimization algorithms 、 Neural networks predict 、 signal processing 、 Cellular automata 、 The image processing 、 Path planning 、 UAV and other fields Matlab Simulation , relevant matlab Code problems can be exchanged by private letter .

Some theories cite network literature , If there is infringement, contact the blogger to delete .