基于小波变换实现图像去噪分析含Matlab源码.zip

function varargout = wavelet_export(varargin) % WAVELET_EXPORT MATLAB code for wavelet_export.fig % WAVELET_EXPORT, by itself, creates a new WAVELET_EXPORT or raises the existing % singleton*. % % H = WAVELET_EXPORT returns the handle to a new WAVELET_EXPORT or the handle to % the existing singleton*. % % WAVELET_EXPORT('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in WAVELET_EXPORT.M with the given input arguments. % % WAVELET_EXPORT('Property','Value',...) creates a new WAVELET_EXPORT or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before wavelet_export_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to wavelet_export_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help wavelet_export % Last Modified by GUIDE v2.5 11-Jan-2014 03:59:01 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @wavelet_export_OpeningFcn, ... 'gui_OutputFcn', @wavelet_export_OutputFcn, ... 'gui_LayoutFcn', @wavelet_export_LayoutFcn, ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT % --- Executes just before wavelet_export is made visible. function wavelet_export_OpeningFcn(hObject, eventdata, handles, varargin) % This function has no output args, see OutputFcn. % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to wavelet_export (see VARARGIN) % Choose default command line output for wavelet_export handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes wavelet_export wait for user response (see UIRESUME) % uiwait(handles.figure_wavelet); setappdata(handles.figure_wavelet,'img_src',0) setappdata(handles.figure_wavelet,'WAVELET_NAME',0); setappdata(handles.figure_wavelet,'Filter_style',0); setappdata(handles.figure_wavelet,'Image_R',0); % --- Outputs from this function are returned to the command line. function varargout = wavelet_export_OutputFcn(hObject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT); % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Get default command line output from handles structure varargout{1} = handles.output; % --- Executes on button press in Import_image. function Import_image_Callback(hObject, eventdata, handles) % hObject handle to Import_image (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) %**********************************************************************% % 一、导入图片 %**********************************************************************% [filename, pathname] = uigetfile( ... {'*.bmp;*,jpg;*.png;*.jpeg;*.tif','Image File(*.bmp,*.jpg,*.png,*jpeg,*.tif)';... '*.*', 'All File(*.*)'},... 'Pick an image'); axes(handles.axes_original); %用axes命令设定当前操作的坐标轴是axes_src fpath = [pathname filename]; %将文件名和目录名组合成一个完整的路径 img_src = imread(fpath); % load img_src; % colormap(map); %MAP矩阵映射当前图形的色图 imshow(img_src); %用imread读入图片，并用imshow在axes_src上显示 setappdata(handles.figure_wavelet,'img_src',img_src); % --- Executes on button press in analyze_image. function analyze_image_Callback(hObject, eventdata, handles) % hObject handle to analyze_image (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) %Main img_src = getappdata(handles.figure_wavelet,'img_src'); %[ X,map] = rgb2ind( img_src, 256);%把RGB图转化为索引图像，得到颜色矩阵map %save '00' X map; load 00; %colormap( map); HANDDLE_IMAGE = img_src; %image(HANDDLE_IMAGE); %**********************************************************************% % 二、选择小波的基波，计算出相关的滤波函数 %**********************************************************************% WAVELET_NAME = getappdata(handles.figure_wavelet,'WAVELET_NAME'); %WAVELET_NAME = 'sym8'; [Lo_D,Hi_D,Lo_R,Hi_R]=wfilters(WAVELET_NAME); RowLo_DTemp = RanksSampling(RanksConv(HANDDLE_IMAGE,Lo_D,'r'),'c',2); %**********************************************************************% % 三、分解 %**********************************************************************% CA = RanksSampling(RanksConv(RowLo_DTemp,Lo_D,'c'),'r',2); RowLo_DTemp = RanksSampling(RanksConv(HANDDLE_IMAGE,Lo_D,'r'),'c',2); CA = RanksSampling(RanksConv(RowLo_DTemp,Lo_D,'c'),'r',2); CH = RanksSampling(RanksConv(RowLo_DTemp,Hi_D,'c'),'r',2); RowHi_DTemp = RanksSampling(RanksConv(HANDDLE_IMAGE,Hi_D,'r'),'c',2); CV = RanksSampling(RanksConv(RowHi_DTemp,Lo_D,'c'),'r',2); CD = RanksSampling(RanksConv(RowHi_DTemp,Hi_D,'c'),'r',2); %[CA,CH,CV,CD] = dwt2(HANDDLE_IMAGE,WAVELET_NAME); axes(handles.axes_CA); image(CA); axes(handles.axes_CH); image(CH); axes(handles.axes_CV); image(CV); axes(handles.axes_CD); image(CD); %**********************************************************************% % 四、去噪 %**********************************************************************% Filter_style = getappdata(handles.figure_wavelet,'Filter_style'); newCA =CA; if(2 == Filter_style) [THR,SORH,KEEPAPP]=ddencmp('den','wv',CH);%获取去噪过程中的默认阈值（软或硬） newCH=wdencmp('gbl',CH,WAVELET_NAME,2,THR,SORH,KEEPAPP); [THR,SORH,KEEPAPP]=ddencmp('den','wv',CV);%获取去噪过程中的默认阈值（软或硬） newCV=wdencmp('gbl',CV,WAVELET_NAME,2,THR,SORH,KEEPAPP);%用全局阈值对图像去噪 [THR,SORH,KEEPAPP]=ddencmp('den','wv',CD);%获取去噪过程中的默认阈值（软或硬） newCD=wdencmp('gbl',CD,WAVELET_NAME,2,THR,SORH,KEEPAPP);%用全局阈值对图像去噪 end if(3 == Filter_style) %下面用独立阈值选项进行图像的消噪 thr_h=[96.245,97.411];%水平方向阈值 thr_v=[99.321,94.122];%垂直方向阈值 thr_d=[95.762,92.330];%对角方向阈值 thr1=[thr_h;thr_v;thr_d];%三维矩阵，长度为N newCH=wdencmp('lvd',CH,WAVELET_NAME,2,thr1,'s');%选择软阈值 newCV=wdencmp('lvd',CV,WAVELET_NAME,2,thr1,'s'); newCD=wdencmp('lvd',CD,WAVELET_NAME,2,thr1,'s'); end axes(handles.axes_newCA); image(newCA); axes(handles.axes_newCH); image(newCH); axes(handles.axes_newCV); image(newCV); axes(handles.axes_newCD); image(newCD); %**********************************************************************% % 五、重构 %**********************************************************************% CA_Temp = RanksInterpolation(newCA,'r',2); CH_Temp = RanksInterpolation(newCH,'r',2); CloumnTemp1 = RanksConv(CA_Temp,Lo_R,'c')+RanksConv(CH_Temp,Hi_R,'c'); CV_Temp = RanksInterpolation(newCV,'r',2); CD_Temp = RanksInterpolation(newCD,'r',2); CloumnTemp2 = RanksConv(CV_Temp,Lo_R,'c')+RanksConv(CD_Temp,Hi_R,'c'); CloumnTempA = RanksInterpolation(CloumnTemp1,'c',2); CloumnTempB = RanksInterpolation(CloumnTemp2,'c',2); RowTemp = RanksConv(CloumnTempA,Lo_R,'r')+RanksConv(CloumnTempB,Hi_R,'r'); Image_R =wkeep(RowTemp,size(HANDDLE_