% denoise2.m
%---本程序是基于LeventCode平臺(tái)作的。
%---包括以下方法：
% BivaShrink方法、模型1、模型2、模型3（TrivaShrink方法）、BayesShrink方法、LAWMLShrink方法的DWT實(shí)現(xiàn)和DT_CWT實(shí)現(xiàn)。
%---運(yùn)行方法：
% 將某方法對(duì)應(yīng)行的代碼前的“%”去掉，而其他方法前一律都加“%”屏蔽掉。
% 噪聲方差大小可以自行設(shè)定

I=imread('lena.bmp');%讀取圖像數(shù)據(jù)
figure;
imshow(I,256);%顯示原圖像

%產(chǎn)生噪聲圖像
theta_noise=20;%噪聲方差(可設(shè)為其他值）------------------------------------------
noise=GWN2(length(I),theta_noise);
Inoise=double(I)+noise;
figure;
imshow(Inoise,[]);%顯示帶噪圖像

%%%%%%%%%%%%%%%%%%%%%%--------BivaShrink方法---------%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%----------Levent原創(chuàng)Bivariate Shrinkage方法：denoising_dwt-----------------------
Idenoise=denoising_dwt(Inoise);
%---------------------------------------------------------------------------------

%----------復(fù)數(shù)小波改進(jìn)-----------------------------------------------------------
%Idenoise=denoising_dtdwt(Inoise);
%---------------------------------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%--------Cai:模型1方法---------%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%----------Cai:這里用當(dāng)前系數(shù)y1和領(lǐng)域系數(shù)y3:BivaShrink13(模型1)-------------------
%Idenoise=BivaShrink13(Inoise);
%---------------------------------------------------------------------------------

%----------復(fù)數(shù)小波改進(jìn)-----------------------------------------------------------
%Idenoise=dt_BivaShrink13(Inoise);
%---------------------------------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%--------Cai:模型2方法---------%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%----------Cai:這里用父層系數(shù)y2和鄰域系數(shù)y3:BivaShrink23--------------------------
%Idenoise=BivaShrink23(Inoise);
%---------------------------------------------------------------------------------

%----------復(fù)數(shù)小波改進(jìn)-----------------------------------------------------------
%Idenoise=dt_BivaShrink23(Inoise);
%---------------------------------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%--------Cai:模型3(TrivaShrink方法)---------%%%%%%%%%%%%%%%%%%%%%%%
%----------Cai:這里用當(dāng)前系數(shù)y1、父層系數(shù)y2和鄰域系數(shù)y3---------------------------
%----------是一種三維的萎縮函數(shù):BivaShrink123-------------------------------------
%Idenoise=BivaShrink123(Inoise);
%---------------------------------------------------------------------------------

%----------復(fù)數(shù)小波改進(jìn)-----------------------------------------------------------
%Idenoise=dt_BivaShrink123(Inoise);
%---------------------------------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%--------BayesShrink---------%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%-------------BayesShrink的方差局部自適應(yīng)版本－-----------------------------------
%Idenoise=denoising_BayesShrink(Inoise);
%---------------------------------------------------------------------------------

%----------復(fù)數(shù)小波改進(jìn)-----------------------------------------------------------
%Idenoise=dt_BayesShrink(Inoise);
%---------------------------------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%--------LAWMLShrink---------%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%--------------LAWMLShrink--------------------------------------------------------
%Idenoise=denoising_LAWMLShrink(Inoise);
%---------------------------------------------------------------------------------

%----------復(fù)數(shù)小波改進(jìn)-----------------------------------------------------------
%Idenoise=dt_LAWMLShrink(Inoise);
%---------------------------------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%輸出去噪后的MSE和PSNR值
MSE2_=MSE2(double(I),Idenoise)
PSNR2=PSNR(MSE2_)
figure;
imshow(Idenoise,[]);%顯示恢復(fù)圖像