gam_noise_gen.zip_GAMMA分布_NOISE_gam_noise_gen_gamma_gamma noise

function y = gam_noise_gen(M, ALPHA, A, B) %%%%%%%%%%%%%%% 获得均值 =M ,有效视数 =ALPHA 的Gamma Noise%%%%%%%%%%%% %input: M 均值 % ALPHA 有效视数, 对应Matlab函数[mean,var] = Gamstat(A,B)函数的参数A：形状参数 % B:var,即方差. 均值＝A×B；方差＝A×B^2。从而可以从A和B换算出mean, var。 % % A 矩阵行数 % B 矩阵列数 %output: y A×B大小的，服从Gamma分布的噪声矩阵 t=1:1:A*B; size_t = length(t); %%常数%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %ALPHA = 4.0; %代表有效视数L, 为保证Mean =1, 一般VAR = ALPHA %M = 2; %%计算准备%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Gamma_nosie = zeros(1,size_t); %%计算%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for k = 1:ALPHA Rand_A1_Col = rand(1,size_t); Noise_A1_Col = (-1)*(M/ALPHA)*log(1-Rand_A1_Col); Gamma_nosie=Gamma_nosie+Noise_A1_Col; end Gamma_noise= reshape(Gamma_nosie,A,B);%获得大小为A×B的Gamma噪声矩阵 y = Gamma_noise; %%显示%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %imshow(y); %Gammacof = 100; %放大比例 %tmp = uint16(y*Gammacof); %%直方图 %figure; %[temp,x]=imhist(uint16(tmp),65536); %His_Org = temp'; %His_Org = His_Org/sum(His_Org);%归一化 %MaxValue =double(max(max(tmp))); %His_Org = His_Org(1:MaxValue); %X_axis = 0:1:MaxValue-1; %X_axis = X_axis/Gammacof; %plot(X_axis,His_Org);