【图像处理】基于matlab GUI Hough变换+PDE图像去雨（带面板）【含Matlab源码 811期】

一、图像处理简介

图像处理知识点：
1 数字图像处理及matlab实现知识点总结1-4
2 数字图像处理及matlab实现知识点总结 5-10

二、部分源代码

function varargout = GUI_part(varargin)
% GUI_PART MATLAB code for GUI_part.fig
%      GUI_PART, by itself, creates a new GUI_PART or raises the existing
%      singleton*.
%
%      H = GUI_PART returns the handle to a new GUI_PART or the handle to
%      the existing singleton*.
%
%      GUI_PART('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in GUI_PART.M with the given input arguments.
%
%      GUI_PART('Property','Value',...) creates a new GUI_PART or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before GUI_part_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to GUI_part_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 GUI_part

% Last Modified by GUIDE v2.5 16-Apr-2014 18:09:28

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
    'gui_Singleton',  gui_Singleton, ...
    'gui_OpeningFcn', @GUI_part_OpeningFcn, ...
    'gui_OutputFcn',  @GUI_part_OutputFcn, ...
    'gui_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 GUI_part is made visible.
function GUI_part_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 GUI_part (see VARARGIN)

% Choose default command line output for GUI_part
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes GUI_part wait for user response (see UIRESUME)
% uiwait(handles.figure1);


% --- Outputs from this function are returned to the command line.
function varargout = GUI_part_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 UI_Load.
function UI_Load_Callback(hObject, eventdata, handles)
% hObject    handle to UI_Load (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
clc;
global IMG;
[filename, pathname] =  uigetfile({'*.tif'; '*.jpg'; '*.bmp';'*.*'},'File Selector');
if isequal(filename,0)
    msgbox(sprintf('Please select image :)'),'No Image Selected','warn');
    return;
end

IMG =imread(filename);
axes(handles.UI_origin);
imshow(IMG);



% --- Executes on button press in UI_process.
function UI_process_Callback(hObject, eventdata, handles)
% hObject    handle to UI_process (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
global IMG;

%generate detected image and tag image
[Img, Img_tag] = GeneratorCore(IMG); %use Hough transform to detect rain and tag area

axes(handles.UI_hough);
imshow(Img);
axes(handles.UI_mask);
imshow(Img_tag);

mu = 20;
[xlen,ylen, ~] =size(Img_tag);
IMG2= imresize(IMG,[max(xlen,ylen),max(xlen,ylen)]);
for i = 1:3
    IMGt = IMG2(:,:,i)
    Origin_resize = double(imresize(IMGt,[max(size(IMGt)),max(size(IMGt))]));
    [x,y] = size(Origin_resize);
    Structure_img(:,:,i) = uint8(reshape(SB_ATV(Origin_resize, mu), x, y)); %use PDE to get structure information
    Texture_img(:,:,i) = uint8(Origin_resize-double(Structure_img(:,:,i)));
end
% figure;subplot(1,2,1);
axes(handles.UI_PDE_low);
imshow(Structure_img);
% title('Structure Image');

% Texture_img = Origin_resize-Structure_img;
% subplot(1,2,2);
axes(handles.UI_PDE_high);
imshow(Texture_img);
% title('Texture Image');

mask = imresize(Img_tag,[max(xlen,ylen),max(xlen,ylen)]);

%Use texture patch to repair image
for i = 1:3
    New_i(:,:,i)= Texture_core(double(Structure_img(:,:,i)),double(mask));
    New_i(:,:,i) = medfilt2(New_i(:,:,i),[5,5]);
end
% figure;
axes(handles.UI_final);
imshow(uint8(New_i),[0,255]);


% --- Executes on button press in UI_hough_step.
function UI_hough_step_Callback(hObject, eventdata, handles)
% hObject    handle to UI_hough_step (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hint: get(hObject,'Value') returns toggle state of UI_hough_step
global IMG;
if( get(hObject,'Value') == 1)
    [Img, Img_tag] = GeneratorCore(IMG); %use Hough transform to detect rain and tag area
    figure;
    subplot(1,2,1);
    imshow(Img);
    subplot(1,2,2);
    imshow(Img_tag);
end



% --- Executes on button press in radiobutton2.
function radiobutton2_Callback(hObject, eventdata, handles)
% hObject    handle to radiobutton2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
unction B=inpaint_nans(A,method)
% INPAINT_NANS: in-paints over nans in an array
% usage: B=INPAINT_NANS(A)          % default method
% usage: B=INPAINT_NANS(A,method)   % specify method used
%
% Solves approximation to one of several pdes to
% interpolate and extrapolate holes in an array
%
% arguments (input):
%   A - nxm array with some NaNs to be filled in
%
%   method - (OPTIONAL) scalar numeric flag - specifies
%       which approach (or physical metaphor to use
%       for the interpolation.) All methods are capable
%       of extrapolation, some are better than others.
%       There are also speed differences, as well as
%       accuracy differences for smooth surfaces.
%
%       methods {0,1,2} use a simple plate metaphor.
%       method  3 uses a better plate equation,
%                 but may be much slower and uses
%                 more memory.
%       method  4 uses a spring metaphor.
%       method  5 is an 8 neighbor average, with no
%                 rationale behind it compared to the
%                 other methods. I do not recommend
%                 its use.
%
%       method == 0 --> (DEFAULT) see method 1, but
%         this method does not build as large of a
%         linear system in the case of only a few
%         NaNs in a large array.
%         Extrapolation behavior is linear.
%         
%       method == 1 --> simple approach, applies del^2
%         over the entire array, then drops those parts
%         of the array which do not have any contact with
%         NaNs. Uses a least squares approach, but it
%         does not modify known values.
%         In the case of small arrays, this method is
%         quite fast as it does very little extra work.
%         Extrapolation behavior is linear.
%         
%       method == 2 --> uses del^2, but solving a direct
%         linear system of equations for nan elements.
%         This method will be the fastest possible for
%         large systems since it uses the sparsest
%         possible system of equations. Not a least
%         squares approach, so it may be least robust
%         to noise on the boundaries of any holes.
%         This method will also be least able to
%         interpolate accurately for smooth surfaces.
%         Extrapolation behavior is linear.
%
%         Note: method 2 has problems in 1-d, so this
%         method is disabled for vector inputs.
%         
%       method == 3 --+ See method 0, but uses del^4 for
%         the interpolating operator. This may result
%         in more accurate interpolations, at some cost
%         in speed.
%         
%       method == 4 --+ Uses a spring metaphor. Assumes
%         springs (with a nominal length of zero)
%         connect each node with every neighbor
%         (horizontally, vertically and diagonally)
%         Since each node tries to be like its neighbors,
%         extrapolation is as a constant function where
%         this is consistent with the neighboring nodes.
%
%       method == 5 --+ See method 2, but use an average
%         of the 8 nearest neighbors to any element.
%         This method is NOT recommended for use.
%
%
% arguments (output):
%   B - nxm array with NaNs replaced
%
%
% Example:
%  [x,y] = meshgrid(0:.01:1);
%  z0 = exp(x+y);
%  znan = z0;
%  znan(20:50,40:70) = NaN;
%  znan(30:90,5:10) = NaN;
%  znan(70:75,40:90) = NaN;
%
%  z = inpaint_nans(znan);
%
%
% See also: griddata, interp1
%
% Author: John D'Errico
% e-mail address: woodchips@rochester.rr.com
% Release: 2
% Release date: 4/15/06


% I always need to know which elements are NaN,
% and what size the array is for any method
[n,m]=size(A);
A=A(:);
nm=n*m;
k=isnan(A(:));

% list the nodes which are known, and which will
% be interpolated
nan_list=find(k);
known_list=find(~k);

% how many nans overall
nan_count=length(nan_list);

% convert NaN indices to (r,c) form
% nan_list==find(k) are the unrolled (linear) indices
% (row,column) form
[nr,nc]=ind2sub([n,m],nan_list);

% both forms of index in one array:
% column 1 == unrolled index
% column 2 == row index
% column 3 == column index
nan_list=[nan_list,nr,nc];

% supply default method
if (nargin<2) || isempty(method)
  method = 0;
elseif ~ismember(method,0:5)
  error 'If supplied, method must be one of: {0,1,2,3,4,5}.'
end

% for different methods
switch method
 case 0
  % The same as method == 1, except only work on those
  % elements which are NaN, or at least touch a NaN.
  
  % is it 1-d or 2-d?
  if (m == 1) || (n == 1)
    % really a 1-d case
    work_list = nan_list(:,1);
    work_list = unique([work_list;work_list - 1;work_list + 1]);
    work_list(work_list <= 1) = [];
    work_list(work_list >= nm) = [];
    nw = numel(work_list);
    
    u = (1:nw)';
    fda = sparse(repmat(u,1,3),bsxfun(@plus,work_list,-1:1), ...
      repmat([1 -2 1],nw,1),nw,nm);
  else
    % a 2-d case
    
    % horizontal and vertical neighbors only
    talks_to = [-1 0;0 -1;1 0;0 1];
    neighbors_list=identify_neighbors(n,m,nan_list,talks_to);
    
    % list of all nodes we have identified
    all_list=[nan_list;neighbors_list];
    
    % generate sparse array with second partials on row
    % variable for each element in either list, but only
    % for those nodes which have a row index > 1 or < n
    L = find((all_list(:,2) > 1) & (all_list(:,2) < n));
    nl=length(L);
    if nl>0
      fda=sparse(repmat(all_list(L,1),1,3), ...
        repmat(all_list(L,1),1,3)+repmat([-1 0 1],nl,1), ...
        repmat([1 -2 1],nl,1),nm,nm);
    else
      fda=spalloc(n*m,n*m,size(all_list,1)*5);
    end

三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 蔡利梅.MATLAB图像处理——理论、算法与实例分析[M].清华大学出版社，2020.
[2]杨丹,赵海滨,龙哲.MATLAB图像处理实例详解[M].清华大学出版社，2013.
[3]周品.MATLAB图像处理与图形用户界面设计[M].清华大学出版社，2013.
[4]刘成龙.精通MATLAB图像处理[M].清华大学出版社，2015.
[5]陈浩,方勇,朱大洲,王成,陈子龙.基于蚁群算法的玉米植株热红外图像边缘检测[J].农机化研究. 2015,37(06)

文章来源: qq912100926.blog.csdn.net，作者：海神之光，版权归原作者所有，如需转载，请联系作者。

原文链接：qq912100926.blog.csdn.net/article/details/116009941