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我编写了以下代码作为在一篇名为“E. Arce-Santana, D. Campos-Delgado and A. Alba, Affine image registrationguided byparticle filter , IET Image Process. 6 (2012 ) 的文章中介绍的配准算法的实现。 )”。

问题一：当我运行代码时，无论我选择多少粒子或迭代，输出仍然不准确。不知道是什么问题？

问题 2：我评论了一个用于更新粒子权重的公式，我的问题是，我是否实现了等式写入或出现在它上面的那个（依赖于熵）是正确的，因此删除评论的那个并留下基于熵的方程？

代码如下：

%% clear everything
clear all
close all
clc

%% Read the image data

% I1: Reference Image
I1=imread('cameraman.tif');
I1=double(imresize(I1,[256 256]));
figure,imshow(I1)
[I1r I1c I1d]=size(I1);

%I2: Target image
I2=randomtransform(I1); %user-defined function to generate random transformation 
% I2=double(imresize(I2,[I1r I1c]));
figure,imshow(I2)

%% Particle Filter Steps:
%%%%% Input:
n=4;         % Related to the initialization
m=256;       % Related to the initialization
N=20;       %(no. of iteration)
M=100;       %(no. of particles)
phai=[];     %(state vector of the affine transformation parameters)
w=[];        %(the vector of the phai weights)
%k: iteration no.
%i: particle no.
Vk=1;        % noise variance in the system (Used in the predection state)
w_v=1;       % weights update equation related variance
beta=0.99;   % annealing factor
%%%%% Output
phai_est=[]; %(Final estimated state vector of affine parameters)

%%%%% Steps:
%%% Step 1: Random generation of the particles
% The ranges are obtained from the paper
    rotationAngle=double(int8(unifrnd(-pi/4,pi/4,1,1)));
    scalingCoefX = double(unifrnd(0.5,2,1,1));
    scalingCoefY = double(unifrnd(0.5,2,1,1));
    shearingCoefX = double(unifrnd(0.5,2,1,1));
    shearingCoefY = double(unifrnd(0.5,2,1,1));
    translationCoefX = double(int8(unifrnd(-I1r/2,I1r/2,1,1)));
    translationCoefY = double(int8(unifrnd(-I1c/2,I1c/2,1,1)));
    %The initialization of the first phai
    phai(1,:)=[round(rotationAngle*10^n)/10^n, round(scalingCoefX*10^n)/10^n, round(scalingCoefY*10^n)/10^n, round(shearingCoefX*10^n)/10^n, round(shearingCoefY*10^n)/10^n, round(translationCoefX*10^n)/10^n, round(translationCoefY*10^n)/10^n]';
    %Make the randomly generated particles from the initial prior gaussian distribution
    for i = 1:M
    phai(i,:) = phai(1,:) + sqrt(2) * randn; %2: the variance of the initial esimate
    w(i,:)=1/M;
    end
    % Normalize the weights:
    w = w./sum(w);

%%% Step2: Resample process
for k=1:N
   for i=1:M
       % rand: u (Uniform random value between 0 and 1
       j=find((cumsum(w) >= max(w)),1,'first');
       phai_select(i,:)=phai(j,:);
       phai(i,:)=phai_select(i,:)+(sqrt(Vk^2)*randn);
       I2_new=targettransform(I2,phai(i,:)); %user-defined function to apply the generated transformation to the target image 
       E_I1=entropy(I1);
       I=E_I1+entropy(I2_new)-joint_entropy(I1,I2_new); %joint_entropy: user defined function to calculate joint entropy of the two images
       w(i)=(1/sqrt(2*pi*w_v))*exp(-((E_I1-I)^2)/(2*w_v));
%        w(i)=prod(prod(((1/sqrt(2*pi*w_v))*exp(-((I2_new-I1)^2)/(2*w_v)))));
   end
       % Normalize the weights
       w = w./sum(w);
       % Reduce the noise standard deviation
       Vk=beta*Vk;
       phai_est=mean(phai);
end
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