SNR功能：
% function to calculate the drop in SNR between Best focussed image to
% slightly out of focus image. The value is presented in dB scale and amplitude
%for comparison.
% Terms:
%%Signal image = best focussed image
%%Noise image = slight off focussed image
%%Dark scan = for future reference with respect to Signal image
%---------------Define function to calcuate SNR---------------%
function SNR = SNR(signal, noise, typ, noisy)
% snr - calculate signal-to-noise ratio in decibel or amplitude
%
% Syntax: SNR = snr(signal, noise)
% SNR = snr(signal, signal_noise, typ, true)
%
% Inputs:
% signal - signal amplitude
% noise - noise amplitude or noisy signal
% typ - type of SNR (default:'db' to decibel or 'amp' to amplitude)
% noisy - eval noise flag (use noise as noisy signal)
%
% Outputs:
% SNR - Signal-to-Noise Ratio
%
% Example:
% dt = 0.01;
% T = 0:dt:10;
% sig = sin(2*pi*T);
% noisy = sig + (0 + .5 * randn(1,length(T))); % error mean 0 and sd .5
% snr_db = snr(sig,noisy,'db',true)
%
% Other m-files required: none
% Subfunctions: rms
%------------------------------- BEGIN CODE -------------------------------
if ~exist('typ', 'var')
typ = 'db';
end
if ~exist('noisy', 'var')
noisy = false;
end
if noisy % eval noise
noise = signal-noise;
end
if strcmp(typ,'db')
SNR = 20*log10(rms(signal)/rms(noise));
elseif strcmp(typ,'amp')% string comparison for type value.
SNR = rms(signal)/rms(noise);
end
end
%-------------------------------- END CODE --------------------------------
RMS功能
function RMS= rms(varargin)
%
% Written by Phillip M. Feldman March 31, 2006
%
% rms computes the root-mean-square (RMS) of values supplied as a
% vector, matrix, or list of discrete values (scalars). If the input is
% a matrix, rms returns a row vector containing the RMS of each column.
% David Feldman proposed the following simpler function definition:
%
% RMS = sqrt(mean([varargin{:}].^2))
%
% With this definition, the function accepts ([1,2],[3,4]) as input,
% producing 2.7386 (this is the same result that one would get with
% input of (1,2,3,4). I'm not sure how the function should behave for
% input of ([1,2],[3,4]). Probably it should produce the vector
% [rms(1,3) rms(2,4)]. For the moment, however, my code simply produces
% an error message when the input is a list that contains one or more
% non-scalars.
if (nargin == 0)
error('Missing input.');
end
% Section 1: Restructure input to create x vector.
if (nargin == 1)
x= varargin{1};
else
for i= 1 : size(varargin,2)
if (prod(size(varargin{i})) ~= 1)
error(['When input is provided as a list, ' ...
'list elements must be scalar.']);
end
x(i)= varargin{i};
end
end
% Section 2: Compute RMS value of x.
RMS= sqrt (mean (x .^2) );
脚本
% sig= best focussed image
% noisy= off focussed image
% dark = no light image
%-------------------------
% calculate SNR between:
% sig and noise
% signal and dark
% noise and dark
clear
sig = rgb2gray(imread('S1-BestFocus.bmp'));
noisy = rgb2gray(imread('S1-OffFocus.bmp'));
dark=rgb2gray(imread('DarkScan.bmp'));
sig_noise = SNR(sig,noisy,'db',true);
sig_dark = SNR(sig,dark,'db',true);
noise_dark = SNR(noisy,dark,'db',true);