【图像修复】基于matlab GUI FMM+Criminisi算法彩色图像修复【含Matlab源码 1507期】
【摘要】
一、FMM+Criminisi算法简介
1 FMM算法 FMM算法是由Telea在2004年提出的，主要思想是先处理待修复区域边缘的像素，然后逐步向内推进，直到所有空洞点修复完毕。设Λ为待修复区域，Λ...
一、FMM+Criminisi算法简介
1 FMM算法
FMM算法是由Telea在2004年提出的，主要思想是先处理待修复区域边缘的像素，然后逐步向内推进，直到所有空洞点修复完毕。设Λ为待修复区域，Λ为区域Λ的边界，p为区域Λ的任意一点，在点p周围已知图像内选择一邻域U(p）。为了填充更加精确，增加已知像素点q对待填充空洞点p的影响，添加一个权重函数w(p,q），在邻域U(p）尺度较小时，对点p一阶估计：
其中I位像素值，I(q）为q点的梯度，w(p,q)=dir(p,q)dst(p,q)lev(p,q),dit(p,q）为距离因子，反映了已知像素q对待填充空洞点p的距离影响；lev(p,q）为水平集因子，反映了到达时间的影响；dir(p,q）为方向因子，反映了已知像素q对待填充空洞点p的纹理相关性的影响。对边界填充完后，需要不断迭代上述步骤，逐渐收缩边界直至空洞区域修复完毕。
2 Criminisi 算法
Criminisi 算法将图像结构信息作为图像修复顺序参考，在修复过程中能根据图像信息合理安排修复顺序。Criminisi 算法除了对大区域破损图像的修复有较好效果，其执行效率也有明显优势。
2.1 Criminisi 算法原理
2.2 算法流程
Criminisi 算法流程关键在于破损区域的填补顺序。实现步骤为：
二、部分源代码
function varargout = mygui(varargin)
% MYGUI MATLAB code for mygui.fig
% MYGUI, by itself, creates a new MYGUI or raises the existing
% singleton*.
%
% H = MYGUI returns the handle to a new MYGUI or the handle to
% the existing singleton*.
%
% MYGUI('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in MYGUI.M with the given input arguments.
%
% MYGUI('Property','Value',...) creates a new MYGUI or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before mygui_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to mygui_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 mygui
% Last Modified by GUIDE v2.5 25-Nov-2021 09:35:16
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @mygui_OpeningFcn, ...
'gui_OutputFcn', @mygui_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 mygui is made visible.
function mygui_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 mygui (see VARARGIN)
% Choose default command line output for mygui
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes mygui wait for user response (see UIRESUME)
% uiwait(handles.figure1);
addpath(genpath('FFM_inpaint'));
addpath(genpath('get_mask'));
addpath(genpath('criminisi_inpaint'));
set(figure,'Visible','off');
global I1_g I2_g I1_mask I2_mask;
I1_mask=[];
I2_mask=[];
img1 = imread('img1.JPG');
img1 = imresize(img1, 0.4); %为了提高运算速度，缩小图片
I1_g = img1;
img2 = imread('img2.JPG');
img2 = imresize(img2, 0.2); %缩小图片
I2_g = img2;
axes(handles.axes1);
imshow(img1);
% --- Outputs from this function are returned to the command line.
function varargout = mygui_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 selection change in popupmenu1.
function popupmenu1_Callback(hObject, eventdata, handles)
% hObject handle to popupmenu1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns popupmenu1 contents as cell array
% contents{get(hObject,'Value')} returns selected item from popupmenu1
global I1_g I2_g;
pic_num=get(hObject,'Value');
if pic_num==1
img = I1_g;
else
img = I2_g;
end
axes(handles.axes1);
imshow(img);
% --- Executes during object creation, after setting all properties.
function popupmenu1_CreateFcn(hObject, eventdata, handles)
% hObject handle to popupmenu1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton_mask.
function pushbutton_mask_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton_mask (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global I1_g I2_g I1_mask I2_mask line2_thick_mask;
pic_num=get(handles.popupmenu1,'Value');
if pic_num==1
I1_gray = rgb2gray(I1_g);
I1_mask = get_mask_1(I1_gray, floor(509*size(I1_gray,1)/719));
else
end
function BW = imbinarize(I,varargin) %#codegen
%imbinarize Binarize image by thresholding.
%
% Syntax
% ------
%
% BW = imbinarize(I)
% BW = imbinarize(I,method)
% BW = imbinarize(I,'adaptive',Param,Value,...)
% BW = imbinarize(I,t)
%
% Input Specs
% -----------
%
% I:
% real
% non-sparse
% 2d
% uint8, uint16, uint32, int8, int16, int32, single or double
%
% method:
% string with value: 'global' or 'adaptive'
% default: 'global'
%
% Sensitivity:
% numeric
% real
% non-sparse
% scalar
% non-negative
% <= 1
% default: 0.5
% converted to double
%
% ForegroundPolarity:
% string with value either 'bright' or 'dark'
% default: 'bright'
%
% t:
% numeric
% real
% non-sparse
% 2d
% either scalar or matrix of the same size as I
%
% Output Specs
% ------------
%
% BW:
% logical
% 2D matrix
% same size as I
%
% Validate the input image
validateImage(I);
% Parse and validate optional inputs
[isNumericThreshold,options] = parseOptionalInputs(I,varargin{:});
coder.internal.prefer_const(isNumericThreshold,options);
if isNumericThreshold
% BW = imbinarize(I,t)
BW = binarize(I,options.t);
else
if strcmp(options.method,'global')
% BW = imbinarize(I,'global')
t = computeGlobalThreshold(I);
else
% BW = imbinarize(I,'adaptive',...)
t = adaptthresh(I,options.sensitivity,'ForegroundPolarity',options.polarity);
end
BW = binarize(I,t);
end
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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]张汝峰,项璟,陈鹏,张亚娟,张喜英,薛瑞.基于FMM算法的深度图像修复研究[J].湖北农机化. 2020,(01)
[6]齐玲,王锦.一种基于Criminisi算法改进的图像修复技术[J].软件导刊. 2019,18(04)
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/121254514
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