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Copy pathImage_Processing_GUI.m
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3650 lines (3392 loc) · 215 KB
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classdef Image_Processing_GUI < matlab.apps.AppBase
% 与应用程序组件对应的属性
properties (Access = public)
%基本组件
figure matlab.ui.Figure
Label_Name matlab.ui.control.Label %姓名
Label_Number matlab.ui.control.Label %学号
globalVar_LEN
globalVar_THETA
globalVar_PSF
globalVar_NCORR
globalVar_ICORR
globalVar_K
globalGau_VAR
globalGau_MEAN
globalDENSITY
global_SPEVAR
global_idealp_Fre
global_ideahp_Fre
global_ideabp_lFre
global_ideabp_hFre
global_ideabs_Fre
global_ideabs_w
global_butterOrder
global_butterlp_Fre
global_butterhp_Fre
global_butterbp_lFre
global_butterbp_hFre
global_butterbs_Fre
global_butterbs_w
global_gausslp_Fre
global_gausshp_Fre
global_gaussbp_lFre
global_gaussbp_hFre
global_gaussbs_Fre
global_gaussbs_w
global_wtThreshold
global_homeOrder %阶数
global_Fre %截止频率
global_hGain %高频增益
global_lGain %低频增益
global_MeanFilter %均值滤波领域大小
global_MedianFilter %中值滤波领域大小
%总变差滤波
global_lambda % 正则化参数，控制去噪程度
global_numIterations % 迭代次数
global_deltaT % 时间步长
global_nlmeansH %去噪强度
global_patchSize %块大小
global_searchWindowSize %搜索窗口大小
Label_PSNR matlab.ui.control.Label %峰值信噪比
Label_MSE matlab.ui.control.Label %均方误差
Label_SNR matlab.ui.control.Label %信噪比
Label_SSIM matlab.ui.control.Label %结构相似性指数
Label_qualityINFO matlab.ui.control.Label %参数说明
Label_QUALITY
uibuttongroup1 matlab.ui.container.ButtonGroup
%文件操作
reset matlab.ui.control.Button
save matlab.ui.control.Button
exit matlab.ui.control.Button
load matlab.ui.control.Button
uibuttongroup2 matlab.ui.container.ButtonGroup
%原始图像
orgimg matlab.ui.control.UIAxes
uibuttongroup3 matlab.ui.container.ButtonGroup
%效果预览图像
effimg matlab.ui.control.UIAxes
uibuttongroup8 matlab.ui.container.ButtonGroup
%频域滤波
p21 matlab.ui.control.Button %理想低通滤波
Label_idealp_Fre matlab.ui.control.Label % 低通截止频率
EditField_idealp_Fre matlab.ui.control.NumericEditField %低通截止频率
p22 matlab.ui.control.Button %理想高通滤波
Label_ideahp_Fre matlab.ui.control.Label % 高通截止频率
EditField_ideahp_Fre matlab.ui.control.NumericEditField %高通截止频率
p23 matlab.ui.control.Button %理想带通滤波
Label_ideabp_lFre matlab.ui.control.Label % 带通低频截止频率
EditField_ideabp_lFre matlab.ui.control.NumericEditField %带通低频截止频率
Label_ideabp_hFre matlab.ui.control.Label % 带通高频截止频率
EditField_ideabp_hFre matlab.ui.control.NumericEditField %带通高频截止频率
p24 matlab.ui.control.Button %理想带阻滤波
Label_ideabs_Fre matlab.ui.control.Label % 带通低频截止频率
EditField_ideabs_Fre matlab.ui.control.NumericEditField %带通低频截止频率
Label_ideabs_w matlab.ui.control.Label % 带通高频截止频率
EditField_ideabs_w matlab.ui.control.NumericEditField %带通高频截止频率
%巴特沃斯滤波器
Label_butterOrder matlab.ui.control.Label % 巴特沃斯滤波器阶数
EditField_butterOrder matlab.ui.control.NumericEditField %巴特沃斯滤波器阶数
p31 matlab.ui.control.Button %巴特沃斯低通
Label_butterlp_Fre matlab.ui.control.Label % 低通截止频率
EditField_butterlp_Fre matlab.ui.control.NumericEditField %低通截止频率
p32 matlab.ui.control.Button %巴特沃斯高通
Label_butterhp_Fre matlab.ui.control.Label % 高通截止频率
EditField_butterhp_Fre matlab.ui.control.NumericEditField %高通截止频率
p33 matlab.ui.control.Button %巴特沃斯带通
Label_butterbp_lFre matlab.ui.control.Label % 带通低频截止频率
EditField_butterbp_lFre matlab.ui.control.NumericEditField %带通低频截止频率
Label_butterbp_hFre matlab.ui.control.Label % 带通高频截止频率
EditField_butterbp_hFre matlab.ui.control.NumericEditField %带通高频截止频率
p34 matlab.ui.control.Button %巴特沃斯带通
Label_butterbs_Fre matlab.ui.control.Label % 带通低频截止频率
EditField_butterbs_Fre matlab.ui.control.NumericEditField %带通低频截止频率
Label_butterbs_w matlab.ui.control.Label % 带通高频截止频率
EditField_butterbs_w matlab.ui.control.NumericEditField %带通高频截止频率
f21 matlab.ui.control.Button %高斯低通滤波
Label_gausslp_Fre matlab.ui.control.Label % 低通截止频率
EditField_gausslp_Fre matlab.ui.control.NumericEditField %低通截止频率
f22 matlab.ui.control.Button %高斯高通滤波
Label_gausshp_Fre matlab.ui.control.Label % 高通截止频率
EditField_gausshp_Fre matlab.ui.control.NumericEditField %高通截止频率
f23 matlab.ui.control.Button %高斯带通滤波
Label_gaussbp_lFre matlab.ui.control.Label % 带通低频截止频率
EditField_gaussbp_lFre matlab.ui.control.NumericEditField %带通低频截止频率
Label_gaussbp_hFre matlab.ui.control.Label % 带通高频截止频率
EditField_gaussbp_hFre matlab.ui.control.NumericEditField %带通高频截止频率
f24 matlab.ui.control.Button %高斯带阻滤波
Label_gaussbs_Fre matlab.ui.control.Label % 带通截止频率
EditField_gaussbs_Fre matlab.ui.control.NumericEditField %带通截止频率
Label_gaussbs_w matlab.ui.control.Label % 阻带带宽
EditField_gaussbs_w matlab.ui.control.NumericEditField % 阻带带宽
p4 matlab.ui.control.Button %小波去噪
Label_wtThreshold matlab.ui.control.Label % 小波去噪阈值
EditField_wtThreshold matlab.ui.control.NumericEditField %小波去噪阈值
homefilter matlab.ui.container.ButtonGroup %同态滤波
p5 matlab.ui.control.Button %同态滤波
Label_homeOrder matlab.ui.control.Label % 同态滤波阶数
EditField_homeOrder matlab.ui.control.NumericEditField %同态滤波阶数
Label_Fre matlab.ui.control.Label % 同态滤波截止频率
EditField_Fre matlab.ui.control.NumericEditField %同态滤波截止频率
Label_hGain matlab.ui.control.Label %同态滤波高频增益
EditField_hGain matlab.ui.control.NumericEditField %同态滤波高频增益
Label_lGain matlab.ui.control.Label % 同态滤波低频增益
EditField_lGain matlab.ui.control.NumericEditField %同态滤波低频增益
uibuttongroup9 matlab.ui.container.ButtonGroup
%空间滤波器/去噪
p1 matlab.ui.control.Button %维纳滤波
Label_Avg matlab.ui.control.Label %均值滤波
Label_MeanFilter matlab.ui.control.Label % 均值滤波领域大小
EditField_MeanFilter matlab.ui.control.NumericEditField % 均值滤波领域大小
f1_1 matlab.ui.control.Button %均值滤波 replicate
f1_2 matlab.ui.control.Button %均值滤波 symmetric
f1_3 matlab.ui.control.Button %均值滤波 circular
f3 matlab.ui.control.Button %中值滤波
Label_MedianFilter matlab.ui.control.Label % 中值滤波领域大小
EditField_MedianFilter matlab.ui.control.NumericEditField % 中值滤波领域大小
f4 matlab.ui.control.Button %非局部均值去噪
Label_nlmeansH matlab.ui.control.Label%去噪强度
EditField_nlmeansH matlab.ui.control.NumericEditField
Label_patchSize matlab.ui.control.Label %块大小
EditField_patchSize matlab.ui.control.NumericEditField
Label_searchWindowSize matlab.ui.control.Label %搜索窗口大小
EditField_searchWindowSize matlab.ui.control.NumericEditField
f5 matlab.ui.control.Button %总变差去噪
Label_lambda matlab.ui.control.Label%去噪强度
EditField_lambda matlab.ui.control.NumericEditField
Label_numIterations matlab.ui.control.Label %块大小
EditField_numIterations matlab.ui.control.NumericEditField
Label_deltaT matlab.ui.control.Label %搜索窗口大小
EditField_deltaT matlab.ui.control.NumericEditField
Label_Wn matlab.ui.control.Label %维纳滤波提示
%添加噪波
uibuttongroup10 matlab.ui.container.ButtonGroup
n1 matlab.ui.control.Button %高斯噪声
Label_MEAN matlab.ui.control.Label %均值
Label_VAR matlab.ui.control.Label %方差
EditField_MEAN matlab.ui.control.NumericEditField %运动长度输入
EditField_VAR matlab.ui.control.NumericEditField %运动方向角输入
n2 matlab.ui.control.Button %泊松噪波
n3 matlab.ui.control.Button %椒盐噪声
Label_DENSITY matlab.ui.control.Label %噪声密度
EditField_DENSITY matlab.ui.control.NumericEditField %噪声密度
n4 matlab.ui.control.Button %斑点噪声
Label_SPEVAR matlab.ui.control.Label %斑点噪声方差
EditField_SPEVAR matlab.ui.control.NumericEditField %噪声密度
n5 matlab.ui.control.Button %运动噪波
Label_LEN matlab.ui.control.Label %运动长度
Label_THETA matlab.ui.control.Label %运动方向角
EditField_LEN matlab.ui.control.NumericEditField %运动长度输入
EditField_THETA matlab.ui.control.NumericEditField %运动方向角输入
uibuttongroup11 matlab.ui.container.ButtonGroup
%变换波形显示
g1 matlab.ui.control.UIAxes %图像RGB颜色分解
g2 matlab.ui.control.UIAxes %傅里叶变换
g3 matlab.ui.control.UIAxes %DCT变换
g4 matlab.ui.control.UIAxes %小波变换1
g5 matlab.ui.control.UIAxes %小波变换2
g6 matlab.ui.control.UIAxes %小波变换3
g7 matlab.ui.control.UIAxes %小波变换4
uibuttongroup12_1 matlab.ui.container.ButtonGroup %小波选择
Label matlab.ui.control.Label
Button1 matlab.ui.control.Button
Button2 matlab.ui.control.Button
Button3 matlab.ui.control.Button
Button4 matlab.ui.control.Button
Button5 matlab.ui.control.Button
Button6 matlab.ui.control.Button
Button7 matlab.ui.control.Button
end
%根据对图片施加的效果更新变换图像
methods (Access = private)
function update(app, handles) %更新图表
% 显示等待条
h = waitbar(0, '更新图像中 等待...');
steps = 30;
for step = 1:steps
waitbar(step / steps)
end
close(h)
mysize=size(handles.img); % 获取处理后的图像大小
if numel(mysize)>2
% 如果图像大小大于二维，则执行 updateColr 函数进行处理
updateColr(app, handles)
updateColr_FT(app, handles)
updateColr_DCT(app, handles)
else
% 否则执行 updateGray 函数进行处理
updateGray(app, handles)
updateGray_FT(app, handles)
updateGary_DCT(app, handles) %更新彩色图像彩色直方图
end
updateDWT(app, handles)
[quality_MSE,quality_SNR,quality_PSNR,quality_SSIM] = calculateQualityMetrics(app,handles.i,handles.img);
app.Label_SSIM.Text = ['SSIM: ', num2str(round(quality_SSIM, 3)), ' dB'];
app.Label_PSNR.Text = ['PSNR: ', num2str(round(quality_PSNR, 3)), ' dB'];
app.Label_SNR.Text = ['SNR: ', num2str(round(quality_SNR, 3)), ' dB'];
app.Label_MSE.Text = ['MSE: ', num2str(round(quality_MSE, 3)), ''];
end
function updateColr(app, handles) %更新彩色图像彩色直方图
set(handles.g1, 'Visible', 'on');
% 将图像数据重塑为一维数组
ImageData1 = mat2gray( handles.img(:,:,1) );
ImageData1 = reshape(ImageData1, [numel(ImageData1), 1]) * 255;
ImageData2 = mat2gray( handles.img(:,:,2) );
ImageData2 = reshape(ImageData2, [numel(ImageData2), 1]) * 255;
ImageData3 = mat2gray( handles.img(:,:,3) );
ImageData3 = reshape(ImageData3, [numel(ImageData3), 1]) * 255;
% 计算直方图
[H1, X1] = imhist(uint8(ImageData1), 256); % 指定横坐标范围为0~255
[H2, X2] = imhist(uint8(ImageData2), 256); % 指定横坐标范围为0~255
[H3, X3] = imhist(uint8(ImageData3), 256); % 指定横坐标范围为0~255
% 在g1示波器中绘制彩色直方图
axes(handles.g1);
cla;
hold on; % 保持绘图区域，以便绘制其他曲线
plot(X1, H1, 'R'); % 绘制红色通道直方图，颜色为红色
plot(X2, H2, 'G'); % 绘制绿色通道直方图，颜色为绿色
plot(X3, H3, 'B'); % 绘制蓝色通道直方图，颜色为蓝色
legend('R','G','B');
title('图像颜色直方图');
maxY = max([H1 H2 H3]);
maxY = max(maxY);
disp(maxY);
axis([0 255 0 maxY-1]); % 设置坐标轴范围，x轴范围为0到256，y轴范围根据直方图的最大值确定
hold off; % 结束绘图区域的保持状态
end
function updateGray(app, handles) %更新灰度直方图
set(handles.g1, 'Visible', 'on');
% 将滤波后的图像数据重塑为一维数组，并将像素值从[0, 1]映射到[0, 255]
ImageData = mat2gray(handles.img);
ImageData = reshape(ImageData, [numel(ImageData), 1]) * 255;
% 计算直方图
[H, X] = imhist(uint8(ImageData), 256); % 指定横坐标范围为0~255
% 在 g1 示波器中绘制灰色直方图
axes(handles.g1);
cla;
hold on;
bar(X, H, 'k');
legend('灰度');
title('图像灰度直方图');
axis([0 255 0 max(H)]);
hold off;
end
function updateColr_FT(app, handles) %更新彩色图像彩色直方图
set(handles.g2, 'Visible', 'on');
% 计算傅里叶变换
imgft = zeros(size(handles.img));
for i = 1:3 % 遍历每个颜色通道
channel = handles.img(:,:,i);
channel_ft = fft2(channel); % 傅里叶变换
imgft(:,:,i) = abs(fftshift(channel_ft)); % 将傅里叶变换的模进行象限转换并保存
end
% 将三个颜色通道的傅里叶变换结果合并或做其他处理
final_imgft = sum(imgft, 3); % 简单地将三个通道的结果相加
% 可视化傅里叶变换结果
imgftam = log(final_imgft + 1); % 将傅里叶变换结果的幅值映射到小的正数
axes(handles.g2);
cla;
imshow(imgftam, []); % 显示傅里叶变换结果图像，映射到[0,1]
title('彩色图像的傅里叶谱');
hold off;
end
function updateGray_FT(app, handles) %更新彩色图像彩色直方图
set(handles.g2, 'Visible', 'on');
% 计算灰度傅里叶变换
grayImg = handles.img;
% 计算傅里叶变换
imgft = fft2(grayImg); % 对灰度图像执行傅里叶变换
imgft = abs(fftshift(imgft)); % 将傅里叶变换的模进行象限转换并保存
% 可视化傅里叶变换结果
imgftam = log(imgft + 1); % 将傅里叶变换结果的幅值映射到小的正数
axes(handles.g2);
cla;
imshow(imgftam, []); % 显示傅里叶变换结果图像，映射到[0,1]
title('灰度图像的傅里叶谱');
hold off;
end
function updateColr_DCT(app, handles) %更新彩色图像彩色直方图
set(handles.g3, 'Visible', 'on');
% 对彩色图像进行DCT变换
dct_img = zeros(size(handles.img));
for i = 1:3 % 遍历每个颜色通道
channel = handles.img(:,:,i);
dct_channel = dct2(channel);
dct_img(:,:,i) = dct_channel;
end
dct_img = log(abs(dct_img));
axes(handles.g3);
cla;
imshow(dct_img, []);
title('彩色图像的DCT频谱');
hold off;
end
function updateGary_DCT(app, handles) %更新彩色图像彩色直方图
set(handles.g3, 'Visible', 'on');
% 对灰色图像进行DCT变换
grayImg = handles.img;
% 计算DCT变换
dctImg = dct2(grayImg);
% 可视化DCT变换结果
axes(handles.g3);
cla;
imshow(log(abs(dctImg) + 1), []); % 显示DCT变换结果的幅值，映射到[0,1]
title('灰度图像的DCT变换');
hold off;
end
function updateDWT(app, handles) %更新小波变换
set(handles.g4, 'Visible', 'on');
set(handles.g5, 'Visible', 'on');
set(handles.g6, 'Visible', 'on');
set(handles.g7, 'Visible', 'on');
% 计算小波变换
mysize=size(handles.img); % 获取处理后的图像大小
if numel(mysize)>2
grayimg = rgb2gray(handles.img);
else
grayimg = handles.img;
end
[grap_LLY, HL, LH, HH] = dwt2(grayimg, 'haar');
[LLY, HL, LH, HH] = dwt2(handles.img, 'haar');
%显示小波变换
set(handles.g4, 'Visible', 'on');
axes(handles.g4);
cla;
imshow(grap_LLY, []);
hold off;
title('小波变换(1.灰度低频近似值)');
set(handles.g5, 'Visible', 'on');
axes(handles.g5);
cla;
imshow(HL, []);
title('2.水平方向细节');
hold off;
set(handles.g6, 'Visible', 'on');
axes(handles.g6);
cla;
imshow(LH, []);
title('3.垂直方向细节');
hold off;
set(handles.g7, 'Visible', 'on');
axes(handles.g7);
cla;
imshow(HH, []);
title('4.对角线方向细节');
hold off;
end
% 定义计算图像质量评价指标的函数
function [quality_MSE,quality_SNR,quality_PSNR,quality_SSIM] = calculateQualityMetrics(app,originalImage, processedImage, metric)
% 将图像转为双精度类型
originalImage = double(originalImage);
processedImage = double(processedImage);
quality_PSNR = calculatePSNR(app,originalImage, processedImage);
quality_SSIM = calculateSSIM(app,originalImage, processedImage);
quality_MSE = calculateMSE(app,originalImage, processedImage);
quality_SNR = calculateSNR(app,originalImage, processedImage);
end
% 定义计算PSNR的函数
function quality_PSNR = calculatePSNR(app,originalImage, processedImage)
% 获取图像大小
[M, N, ~] = size(originalImage);
% 计算均方误差（MSE）
mse = sum(sum((originalImage - processedImage).^2)) / (M * N);
% 计算PSNR
A = 255; % 8比特精度图像的最大像素值
quality_PSNR = 10 * log10(A^2 / mse);
end
% 定义计算SSIM的函数
function quality_SSIM = calculateSSIM(app,originalImage, processedImage)
% 使用 MATLAB 内置的 ssim 函数计算 SSIM
quality_SSIM = ssim(processedImage,originalImage);
end
% 定义计算MSE的函数
function quality_MSE = calculateMSE(app,originalImage, processedImage)
% 获取图像大小
[M, N, ~] = size(originalImage);
% 计算均方误差（MSE）
quality_MSE = sum(sum((originalImage - processedImage).^2)) / (M * N);
end
% 定义计算 SNR 的函数
function quality_SNR = calculateSNR(app,originalImage, processedImage)
% 计算信号强度
signal = sum(sum(originalImage.^2));
% 计算噪声强度
noise = sum(sum((originalImage - processedImage).^2));
% 计算 SNR
quality_SNR = 10 * log10(signal / noise);
end
end
% 句柄控件控制的回调函数
methods (Access = private)
% 组件创建后执行的代码
function Image_processing_GUI_OpeningFcn(app, varargin)
% Create GUIDE-style callback args - Added by Migration Tool
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app); %#ok<ASGLU>
% 选择 Image_processing_GUI 的默认命令行输出
handles.output = hObject;
% 禁用一些按钮和控件
set(handles.load,'Enable','on');
set(handles.save,'Enable','off');
set(handles.exit,'Enable','off');
set(handles.reset,'Enable','off');
set(handles.effimg,'Visible','off');
set(handles.orgimg,'Visible','off');
set(handles.g1,'Visible','off');
set(handles.g2,'Visible','off');
set(handles.g3,'Visible','off');
set(handles.g4,'Visible','off');
set(handles.g5,'Visible','off');
set(handles.g6,'Visible','off');
set(handles.g7,'Visible','off');
set(handles.n1,'Enable','off');
set(handles.n2,'Enable','off');
set(handles.n3,'Enable','off');
set(handles.n4,'Enable','off');
set(handles.f1_1,'Enable','off');
set(handles.f1_2,'Enable','off');
set(handles.f1_3,'Enable','off');
set(handles.f21,'Enable','off');
set(handles.f22,'Enable','off');
set(handles.f23,'Enable','off');
set(handles.f24,'Enable','off');
set(handles.f3,'Enable','off');
set(handles.f4,'Enable','off');
set(handles.f5,'Enable','off');
set(handles.p1,'Enable','off');
set(handles.p21,'Enable','off');
set(handles.p22,'Enable','off');
set(handles.p23,'Enable','off');
set(handles.p24,'Enable','off');
set(handles.p31,'Enable','off');
set(handles.p32,'Enable','off');
set(handles.p33,'Enable','off');
set(handles.p34,'Enable','off');
set(handles.p4,'Enable','off');
set(handles.p5,'Enable','off');
set(handles.uibuttongroup12_1,'visible','off');
set(handles.Button1,'Enable','off');
set(handles.Button2,'Enable','off');
set(handles.Button3,'Enable','off');
set(handles.Button4,'Enable','off');
set(handles.Button5,'Enable','off');
set(handles.Button6,'Enable','off');
set(handles.Button7,'Enable','off');
set(handles.n5,'Enable','off');
% 更新 handles 结构
guidata(hObject, handles);
end
% 加载
function load_Callback(app, event)
% Create GUIDE-style callback args - Added by Migration Tool
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 打开文件选择对话框
[file, path] = uigetfile({'*.jpg;*.bmp;*.jpeg;*.png', '图片文件 (*.jpg,*.bmp,*.jpeg,*.png)'}, '打开文件');
image = [path file];
handles.file = image;
% 如果用户取消选择文件，弹出警告对话框
if (file == 0)
warndlg('您没有选择图片。') ;
return;
end
% 获取文件扩展名
[fpath, fname, fext] = fileparts(file);
validex = {'.bmp', '.jpg', '.jpeg', '.png'};
found = 0;
% 检查文件扩展名是否合法
for x = 1:length(validex)
if (strcmpi(fext, validex{x}))
found = 1;
% 启用一些按钮和控件
set(handles.save, 'Enable', 'on');
set(handles.exit, 'Enable', 'on');
set(handles.reset, 'Enable', 'on');
set(handles.effimg, 'Visible', 'on');
set(handles.orgimg, 'Visible', 'on');
set(handles.n1, 'Enable', 'on');
set(handles.n2, 'Enable', 'on');
set(handles.n3, 'Enable', 'on');
set(handles.n4, 'Enable', 'on');
set(handles.f1_1,'Enable','on');
set(handles.f1_2,'Enable','on');
set(handles.f1_3,'Enable','on');
set(handles.f21, 'Enable', 'on');
set(handles.f22, 'Enable', 'on');
set(handles.f23, 'Enable', 'on');
set(handles.f24, 'Enable', 'on');
set(handles.f3, 'Enable', 'on');
set(handles.f4, 'Enable', 'on');
set(handles.f5, 'Enable', 'on');
set(handles.p21,'Enable','on');
set(handles.p22,'Enable','on');
set(handles.p23,'Enable','on');
set(handles.p24,'Enable','on');
set(handles.p31,'Enable','on');
set(handles.p32,'Enable','on');
set(handles.p33,'Enable','on');
set(handles.p34,'Enable','on');
set(handles.p4, 'Enable', 'on');
set(handles.p5, 'Enable', 'on');
set(handles.Button1,'Enable','on');
set(handles.Button2,'Enable','on');
set(handles.Button3,'Enable','on');
set(handles.Button4,'Enable','on');
set(handles.Button5,'Enable','on');
set(handles.Button6,'Enable','on');
set(handles.Button7,'Enable','on');
set(handles.n5,'Enable','on');
% 读取图像数据
handles.img = imread(image); %handles.img载入图像数据
handles.i = imread(image); %原图数据
% assignin('base', 'myVariable2', handles.img);
% 显示图像在effimg和orgimg中
axes(handles.orgimg); % 设置orgimg为当前绘图区域
cla; % 清空当前绘图区域
imshow(handles.img); % 在orgimg绘图区域显示图像数据
axes(handles.effimg); % 设置effimg为当前绘图区域
cla; % 清空当前绘图区域
imshow(handles.img); % 在effimg绘图区域显示图像数据
% 更新 handles 结构
guidata(hObject, handles);
% 关闭等待条
hWaitbar = waitbar(0, '等待......', 'CreateCancelBtn', 'delete(gcbf)');
set(hWaitbar, 'Color', [0.9, 0.9, 0.9]);
steps = 3;
waitbar(1 / steps);
waitbar(2 / steps);
waitbar(3 / steps);
%关闭进度条
close(hWaitbar);
update(app, handles);
hWaitbar = waitbar(0, '等待......', 'CreateCancelBtn', 'delete(gcbf)');
set(hWaitbar, 'Color', [0.9, 0.9, 0.9]);
steps = 10;
for waiting = 1 : 10
waitbar(waiting / steps);
end
close(hWaitbar);
set(handles.uibuttongroup12_1,'visible','on');
app.globalVar_LEN = 10;
app.globalVar_THETA = 20;
app.globalGau_MEAN = 0;
app.globalGau_VAR = 0.05;
app.globalDENSITY = 0.1;
app.global_SPEVAR = 0.04;
%理想滤波器
app.global_idealp_Fre = 60;
app.global_ideahp_Fre = 30;
app.global_ideabp_lFre = 20;
app.global_ideabp_hFre = 80;
app.global_ideabs_Fre = 50;
app.global_ideabs_w = 30;
%巴特沃斯滤波器
app.global_butterOrder = 6;
app.global_butterlp_Fre = 60;
app.global_butterhp_Fre = 30;
app.global_butterbp_lFre = 20;
app.global_butterbp_hFre = 80;
app.global_butterbs_Fre = 50;
app.global_butterbs_w = 30;
%高斯滤波器
app.global_gausslp_Fre = 60;
app.global_gausshp_Fre = 30;
app.global_gaussbp_lFre = 20;
app.global_gaussbp_hFre = 80;
app.global_gaussbs_Fre = 50;
app.global_gaussbs_w = 30;
%小波去噪
app.global_wtThreshold = 0.5;
%同态滤波
app.global_homeOrder = 1;
app.global_Fre = 5;
app.global_hGain = 1.1;
app.global_lGain = 0.1;
%均值滤波领域大小
app.global_MeanFilter = 6;
%中值滤波领域大小
app.global_MedianFilter = 3;
%总变差滤波
app.global_lambda = 0.1; % 正则化参数，控制去噪程度
app.global_numIterations = 30; % 迭代次数
app.global_deltaT = 0.1; % 时间步长
app.global_nlmeansH = 2; %去噪强度
app.global_patchSize = 5; %块大小
app.global_searchWindowSize = 10; %搜索窗口大小
% 更新 handles 结构
guidata(hObject, handles);
break;
end
end
% 如果文件扩展名不合法，弹出错误对话框
if (found == 0)
errordlg('文件扩展名不正确，请从可用扩展名[.jpg、.jpeg、.bmp、.png]中选择文件','Image Format Error');
end
end
% 退出
function exit_Callback(app, event)
% Create GUIDE-style callback args - Added by Migration Tool
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 关闭所有图形窗口
close all;
end
% 恢复原图
function reset_Callback(app, event)
% Create GUIDE-style callback args - Added by Migration Tool
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 将图像恢复为初始状态
handles.img = handles.i;
% 在effimg中显示图像
axes(handles.effimg);
cla;
imshow(handles.img);
% 更新
update(app, handles);
% 更新 handles 结构
guidata(hObject,handles);
end
% 保存图像
function save_Callback(app, event)
% Create GUIDE-style callback args - Added by Migration Tool
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 弹出文件保存对话框
[file, path] = uiputfile('*.jpg', 'Save Image as');
% 拼接保存路径和文件名
save = [path file];
try
% 将图像保存为 JPG 格式
imwrite(handles.img, save, 'jpg');
catch
warndlg('您没有填写保存图片名称。') ;
return;
end
end
% f1_1 : 均值滤波 replicate
function f1_1_Callback(app, event)
% 定义一个名为 f1_1_Callback 的函数，其输入参数为 app、event 和 neighborhood_size
% 创建 GUIDE 风格的回调函数参数（由迁移工具添加）
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 显示等待条
h = waitbar(0, '等待...');
steps = 200;
for step = 1:steps
waitbar(step / steps)
end
close(h)
mean_size = [app.global_MeanFilter, app.global_MeanFilter];
% 使用 convertToGUIDECallbackArguments 函数将 app 和 event 转换为 GUIDE 回调函数所需的参数，并将其存储在 hObject、eventdata 和 handles 变量中
h = fspecial('average', mean_size);
% 创建一个指定大小的平均滤波器模板
handles.img = imfilter(handles.img, h, 'replicate');
% 使用平均滤波器对图像进行滤波处理
axes(handles.effimg);
cla;
imshow(handles.img)
% 将当前坐标轴更改为 handles.effimg 所代表的坐标轴，清空坐标轴并显示处理后的图像
% 更新 handles 结构
guidata(hObject, handles);
% 显示等待条
h = waitbar(0, '更新图像中 等待...');
steps = 200;
for step = 1:steps
waitbar(step / steps)
end
close(h)
% 更新函数
update(app, handles);
end
% f1_2 : 均值滤波 symmetric
function f1_2_Callback(app, event)
% 创建 GUIDE 风格的回调函数参数（由迁移工具添加）
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 显示等待条
h = waitbar(0, '等待...');
steps = 200;
for step = 1:steps
waitbar(step / steps)
end
close(h)
mean_size = [app.global_MeanFilter, app.global_MeanFilter];
% 使用 convertToGUIDECallbackArguments 函数将 app 和 event 转换为 GUIDE 回调函数所需的参数，并将其存储在 hObject、eventdata 和 handles 变量中
h = fspecial('average', mean_size);
% 创建一个平均滤波器模板
handles.img=imfilter(handles.img,h,'symmetric');
% 使用平均滤波器对图像进行滤波处理
axes(handles.effimg);
cla;
imshow(handles.img)
% 将当前坐标轴更改为 handles.effimg 所代表的坐标轴，清空坐标轴并显示处理后的图像
% 更新 handles 结构
guidata(hObject, handles);
% 显示等待条
h = waitbar(0, '更新图像中 等待...');
steps = 200;
for step = 1:steps
waitbar(step / steps)
end
close(h)
% 更新函数
update(app,handles);
end
% f1_3 : 均值滤波 circular
function f1_3_Callback(app, event)
% 创建 GUIDE 风格的回调函数参数（由迁移工具添加）
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 显示等待条
h = waitbar(0, '等待...');
steps = 200;
for step = 1:steps
waitbar(step / steps)
end
close(h)
mean_size = [app.global_MeanFilter, app.global_MeanFilter];
% 使用 convertToGUIDECallbackArguments 函数将 app 和 event 转换为 GUIDE 回调函数所需的参数，并将其存储在 hObject、eventdata 和 handles 变量中
h = fspecial('average', mean_size);
% 创建一个平均滤波器模板
handles.img=imfilter(handles.img,h,'circular');
% 使用平均滤波器对图像进行滤波处理
axes(handles.effimg);
cla;
imshow(handles.img)
% 将当前坐标轴更改为 handles.effimg 所代表的坐标轴，清空坐标轴并显示处理后的图像
% 更新 handles 结构
guidata(hObject, handles);
% 显示等待条
h = waitbar(0, '更新图像中 等待...');
steps = 200;
for step = 1:steps
waitbar(step / steps)
end
close(h)
% 更新函数
update(app,handles);
end
% Value changed function: EditField_MeanFilterChanged
function EditField_MeanFilterChanged(app, event)
app.global_MeanFilter = app.EditField_MeanFilter.Value;
end
% f3 中值滤波
function f3_Callback(app, event)
% 定义一个名为 f3_Callback 的函数，其输入参数为 app 和 event
% 创建 GUIDE 风格的回调函数参数（由迁移工具添加）
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 显示等待条
h = waitbar(0, '等待...');
steps = 200;
for step = 1:steps
waitbar(step / steps)
end
close(h)
median_size = [app.global_MedianFilter, app.global_MedianFilter];
% 使用 convertToGUIDECallbackArguments 函数将 app 和 event 转换为 GUIDE 回调函数所需的参数，并将其存储在 hObject、eventdata 和 handles 变量中
mysize=size(handles.img); % 获取处理后的图像大小
if numel(mysize)>2
r=medfilt2(handles.img(:,:,1), median_size);
% 对图像的红色通道进行中值滤波处理
g=medfilt2(handles.img(:,:,2), median_size);
% 对图像的绿色通道进行中值滤波处理
b=medfilt2(handles.img(:,:,3), median_size);
% 对图像的蓝色通道进行中值滤波处理
handles.img=cat(3,r,g,b);
else
gray=medfilt2(handles.img(:,:,1), median_size);
handles.img=cat(1, gray);
end
% 将三个滤波处理后的通道重新组合成图像
axes(handles.effimg);
% 将当前坐标轴更改为 handles.effimg 所代表的坐标轴
cla; imshow(handles.img);
% 清空坐标轴并显示处理后的图像
guidata(hObject,handles);
% 更新应用程序数据，将 handles 变量保存到 app 中
% 更新 handles 结构
guidata(hObject, handles);
% 显示等待条
h = waitbar(0, '更新图像中 等待...');
steps = 200;
for step = 1:steps
waitbar(step / steps)
end
close(h)
% 更新函数
update(app,handles);
end
% Value changed function: EditField_MedianFilterChanged
function EditField_MedianFilterChanged(app, event)
app.global_MedianFilter = app.EditField_MedianFilter.Value;
end
%非局部均值去噪
function f4_Callback(app, event)
% 从输入参数中获取对象句柄和其他信息
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 显示等待条
h_waitbar = waitbar(0, '等待... 正在计算,非局部均值去噪计算时间较长，请耐心等待。');
steps = 200;
for step = 0:100
waitbar(step / steps)
end
% 获取图像及相关参数
img = handles.img; % 获取图像
%I:含噪声图像
%ds:邻域窗口半径
%Ds:搜索窗口半径
%h:高斯函数平滑参数
%DenoisedImg：去噪图像
h = app.global_nlmeansH; % 获取非局部均值去噪参数h
ds = app.global_patchSize; % 获取像素块大小参数
Ds = app.global_searchWindowSize; % 获取搜索窗口大小参数
I=double(img);
[m,n]=size(I);
DenoisedImg=zeros(m,n);
PaddedImg = padarray(I,[ds,ds],'symmetric','both');
kernel=ones(2*ds+1,2*ds+1);
kernel=kernel./((2*ds+1)*(2*ds+1));
h2=h*h;
for i=1:m
for j=1:n
i1=i+ds;
j1=j+ds;
W1=PaddedImg(i1-ds:i1+ds,j1-ds:j1+ds);%邻域窗口1
wmax=0;
average=0;
sweight=0;
%%搜索窗口
rmin = max(i1-Ds,ds+1);
rmax = min(i1+Ds,m+ds);
smin = max(j1-Ds,ds+1);
smax = min(j1+Ds,n+ds);
for r=rmin:rmax
for s=smin:smax
if(r==i1&&s==j1)
continue;
end
W2=PaddedImg(r-ds:r+ds,s-ds:s+ds);%邻域窗口2
Dist2=sum(sum(kernel.*(W1-W2).*(W1-W2)));%邻域间距离
w=exp(-Dist2/h2);
if(w>wmax)
wmax=w;
end
sweight=sweight+w;
average=average+w*PaddedImg(r,s);
end
end
average=average+wmax*PaddedImg(i1,j1);%自身取最大权值
sweight=sweight+wmax;
DenoisedImg(i,j)=average/sweight;
end
end
for step = 100:200
waitbar(step / steps)
end
close(h_waitbar)
% 更新图像句柄中的图像数据
handles.img = DenoisedImg;
assignin('base', 'myVariable2', handles.img);
% 在图像窗口中显示处理后的图像
axes(handles.effimg);
cla;
imshow(handles.img,[]);
% 更新图像句柄
guidata(hObject, handles);
% 更新应用程序
update(app,handles);
end
% Value changed function: EditField_lambdaChanged
function EditField_nlmeansHChanged(app, event)
app.global_nlmeansH = app.EditField_nlmeansH.Value;
end
% Value changed function: EditField_numIterationsChanged
function EditField_patchSizeChanged(app, event)
app.global_patchSize = app.EditField_patchSize.Value;
end
% Value changed function: EditField_deltaTChanged
function EditField_searchWindowSizeChanged(app, event)
app.global_searchWindowSize = app.EditField_searchWindowSize.Value;
end
%总变差去噪
function f5_Callback(app, event)
% 创建 GUIDE 风格的回调函数参数
[hObject, eventdata, handles] = convertToGUIDECallbackArguments(app, event); %#ok<ASGLU>
% 显示等待条
h = waitbar(0, '等待...');
steps = 200;
for step = 1:steps
waitbar(step / steps)
end
close(h)
% 获取处理后的图像大小
mysize = size(handles.img);
if numel(mysize) > 2
% 对图像的三个通道分别进行总变差去噪处理
r = total_variation_denoising(app, handles.img(:,:,1)); % 对红色通道进行总变差去噪处理
g = total_variation_denoising(app, handles.img(:,:,2)); % 对绿色通道进行总变差去噪处理
b = total_variation_denoising(app, handles.img(:,:,3)); % 对蓝色通道进行总变差去噪处理
handles.img = cat(3, r, g, b);
else
% 对灰度图像进行总变差去噪处理
gray = total_variation_denoising(app, handles.img(:,:,1));
handles.img = cat(1, gray);
end
% 将处理后的图像显示在坐标轴上