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Content introduction
Image restoration is an important research direction in the field of computer vision. With the development of modern science and technology, image restoration technology has been widely used in various fields, such as digital image processing, medical image analysis, cultural relics protection, etc. Among them, the image repair method based on genetic algorithm has shown good results in processing damaged edges for edge linking.
Genetic algorithm is an optimization algorithm that simulates the biological evolution process. It continuously optimizes solutions in the search space by simulating operations such as natural selection, crossover, and mutation to achieve the goal of solving problems. In image repair, genetic algorithms can be applied to the edge linking problem of damaged edges.
Broken edges refer to the situation where image edges are missing or discontinuous due to image damage or other reasons. Edge linking refers to connecting damaged edges with surrounding intact edges so that the image edges are restored to integrity. The edge linking method based on genetic algorithm finds the best edge linking method by optimizing the solution in the search space to achieve image restoration.
Specifically, the image restoration method based on genetic algorithm includes the following steps:
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Initialization population: Randomly generate a set of initial solutions representing possible edge linking methods.
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Evaluate fitness: According to certain evaluation criteria, evaluate the fitness of each solution. Evaluation criteria can be defined based on specific issues, such as edge continuity, edge smoothness, etc.
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Selection operation: Based on the fitness evaluation results, select a part of excellent solutions as parents for subsequent crossover and mutation operations.
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Crossover operation: randomly select two solutions from the parent generation and generate a new solution through some kind of crossover. Intersection operations can be performed by exchanging edge fragments, merging edges, etc.
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Mutation operation: perform mutation operation on the newly generated solution to introduce a certain degree of randomness. Mutation operations can be performed by changing the shape and position of edges.
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Update the population: Add the newly generated solution to the population and replace the solution with poor fitness.
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Termination condition: Based on the preset termination condition, determine whether the condition for stopping iteration is reached. Termination conditions can be reaching a certain number of iterations, fitness reaching a certain threshold, etc.
Through the iterative operation of the above steps, the image repair method based on genetic algorithm can continuously optimize the solution in the search space, and finally find the best edge linking method to achieve the repair of damaged edges.
The image repair method based on genetic algorithm has good robustness and adaptability in the edge linking problem of damaged edges. It is able to handle various types of edge breakage situations and does not rely on specific image characteristics. In addition, genetic algorithms can also improve the efficiency of the algorithm through parallel computing and other means.
However, there are also some challenges in image restoration methods based on genetic algorithms. First, the performance of the algorithm is highly dependent on the selection of the initial population and the design of the fitness evaluation criterion. Secondly, the algorithm has a slow convergence speed and requires a large number of iterations to achieve good results. In addition, the parameter settings of the algorithm also have a great impact on the performance of the algorithm.
In summary, the image repair method based on genetic algorithm has good application prospects in the edge linking problem of damaged edges. With the continuous development of computer vision and optimization algorithms, it is believed that image restoration methods based on genetic algorithms will play an increasingly important role in the field of image processing.
Part of the code
clc;clear all;close all;</code><code>( zeros 2 gap 1 blk 2 gap 28 zeros---28</code><code>%2 gap--2</code><code>( ones 5 gap 28 ones--1</code><code>%2 gap</code><code>( zeros 2 gap 1 blk 2 gap 28 zeros---28</code><code>% % straight line image</code><code>% a=[zeros(28,30) ones(28,1) zeros(28,30)];</code><code>% b=[zeros(2, 61)];</code><code>% c=[ones(1,28) zeros(1,5) ones(1,28)];</code><code>% n=61;</code><code>% img=[a;b;c;b;a].*255;</code><code>% figure;imshow(uint8(img))</code><code>%% Diagonal image </code><code>n=61;i1=1;i2=n;</code><code>img=zeros(n,n);</code><code>for i=1:28</code> code><code> img(i1,i1)=255;img(i2,i2)=255;img(i1,i2)=255;img(i2,i1)=255;</code><code> i1= i1 + 1;i2=i2-1;</code><code>end</code><code>figure;imshow(uint8(img))</code><code>%%</code><code> mimg=zeros(n,n,3);</code><code>for pl=1:3,mimg(:,:,pl)=img;end</code><code>finaloutimg=zeros(size( img));</code><code>[sm,sn]=size(img);</code><code>for ki=31</code><code> for kj=31</code><code> if img(ki,kj)==0,ki,kj</code><code> h1 = @(cx) edgefitness2(cx, img, ki, kj);</code><code> h2 = @( cx) curve_constraints(cx, ki, kj);</code><code> ga_opts = gaoptimset( 'Generations', 1000);</code><code>% ga_opts = gaoptimset(ga_opts,'TolFun', 1e-10 ,'StallGenLimit', 100, 'FitnessLimit', 1e-5,'display','iter');</code><code>% [cx, err_ga] = ga(h1,12,[],[], [],[],[ones(2,1);zeros(4,1);ones(2,1);zeros(4,1)],repmat(8,12,1),h2,[1: 12],ga_opts); %atleast two neighbors per pixel</code><code> [cx, err_ga] = ga(h1,12,[],[],[],[],[ones(12,1) ],repmat(8,12,1),h2,[1:12],ga_opts);</code><code>% 1 2 3</code><code>% 4 0 5</code><code>% 6 7 8</code><code>% edgemap numbering/angle -- 8 directions</code><code>% 5 pixels neighboring previous to pixel in single direction</code><code> curti=ki;curtj =kj;</code><code> px=zeros(size(cx,2),1);py=zeros(size(cx,2),1);pxl=zeros(size(cx,2),1 );flag1=0;flag2=0;</code><code> for i=1</code><code> if cx(i)==0,flag1=1;end</code><code> if flag1~=1</code><code> if cx(i)==1,px(i)=curti-1;py(i)=curtj-1;pxl(i)=px(i)*py( i);</code><code> elseif cx(i)==2,px(i)=curti-1;py(i)=curtj;pxl(i)=px(i)*py(i); </code><code> elseif cx(i)==3,px(i)=curti-1;py(i)=curtj + 1;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==4,px(i)=curti;py(i)=curtj-1;pxl(i)=px(i)*py(i);</code> <code> elseif cx(i)==5,px(i)=curti;py(i)=curtj + 1;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==6,px(i)=curti + 1;py(i)=curtj-1;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==7,px(i)=curti + 1;py(i)=curtj;pxl(i)=px(i)*py(i);</code><code> elseif cx(i )==8,px(i)=curti + 1;py(i)=curtj + 1;pxl(i)=px(i)*py(i);</code><code> end</code> <code> end</code><code> end</code><code> for i=(size(cx,2)/2) + 1</code><code> if cx(i)==0, flag2=1;end</code><code> if flag2~=1</code><code> if cx(i)==1,px(i)=curti-1;py(i)=curtj-1 ;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==2,px(i)=curti-1;py(i)=curtj;pxl( i)=px(i)*py(i);</code><code> elseif cx(i)==3,px(i)=curti-1;py(i)=curtj + 1;pxl(i )=px(i)*py(i);</code><code> elseif cx(i)==4,px(i)=curti;py(i)=curtj-1;pxl(i)=px (i)*py(i);</code><code> elseif cx(i)==5,px(i)=curti;py(i)=curtj + 1;pxl(i)=px(i) *py(i);</code><code> elseif cx(i)==6,px(i)=curti + 1;py(i)=curtj-1;pxl(i)=px(i)* py(i);</code><code> elseif cx(i)==7,px(i)=curti + 1;py(i)=curtj;pxl(i)=px(i)*py(i );</code><code> elseif cx(i)==8,px(i)=curti + 1;py(i)=curtj + 1;pxl(i)=px(i)*py(i) ;</code><code> end</code><code> end</code><code> end</code><code> for i=2:size(cx,2)/2</code><code> if cx(i)==0,flag1=1;end</code><code> if flag1~=1</code><code> if cx(i)==1,px(i)=px (i-1)-1;py(i)=py(i-1)-1;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)= =2,px(i)=px(i-1)-1;py(i)=py(i-1);pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==3,px(i)=px(i-1)-1;py(i)=py(i-1) + 1;pxl(i)=px(i)*py( i);</code><code> elseif cx(i)==4,px(i)=px(i-1);py(i)=py(i-1)-1;pxl(i)= px(i)*py(i);</code><code> elseif cx(i)==5,px(i)=px(i-1);py(i)=py(i-1) + 1;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==6,px(i)=px(i-1) + 1;py(i )=py(i-1)-1;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==7,px(i)=px(i -1) + 1;py(i)=py(i-1);pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==8,px (i)=px(i-1) + 1;py(i)=py(i-1) + 1;pxl(i)=px(i)*py(i);</code><code> end </code><code> end</code><code> end</code><code> for i=(size(cx,2)/2) + 2:size(cx,2)</code><code> if cx(i)==0,flag2=1;end</code><code> if flag2~=1</code><code> if cx(i)==1,px(i)=px (i-1)-1;py(i)=py(i-1)-1;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)= =2,px(i)=px(i-1)-1;py(i)=py(i-1);pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==3,px(i)=px(i-1)-1;py(i)=py(i-1) + 1;pxl(i)=px(i)*py( i);</code><code> elseif cx(i)==4,px(i)=px(i-1);py(i)=py(i-1)-1;pxl(i)= px(i)*py(i);</code><code> elseif cx(i)==5,px(i)=px(i-1);py(i)=py(i-1) + 1;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==6,px(i)=px(i-1) + 1;py(i )=py(i-1)-1;pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==7,px(i)=px(i -1) + 1;py(i)=py(i-1);pxl(i)=px(i)*py(i);</code><code> elseif cx(i)==8,px (i)=px(i-1) + 1;py(i)=py(i-1) + 1;pxl(i)=px(i)*py(i);</code><code> end </code><code> end</code><code> end</code><code> for ii=1:size(pxl,1)/2</code><code>% if pxl(ii)> 0 & amp; & amp; size(pxl(pxl>0),1)>4</code><code> if pxl(ii)>0</code><code> mpi=px(ii);mpj= py(ii);</code><code> finaloutimg(mpi,mpj)=1;mimg(mpi,mpj,:)=[255,0,0];</code><code> end</code> <code> end</code><code> for ii=(size(pxl,1)/2) + 1:size(pxl,1)</code><code>% if pxl(ii)>0 & amp ; & amp; size(pxl(pxl>0),1)>4</code><code> if pxl(ii)>0</code><code> mpi=px(ii);mpj=py(ii );</code><code> finaloutimg(mpi,mpj)=1;mimg(mpi,mpj,:)=[0,0,255];</code><code> end</code><code> end</code><code> finaloutimg(ki,kj)=1;mimg(ki,kj,:)=[0,255,0];</code><code> end</code><code> end</code> <code>end</code><code>?</code><code>?</code><code>figure;imshow(uint8(mimg));
Operation results
References
[1] Wu Xiaolin, Zhang Dong. Digital image edge detection based on genetic algorithm[J]. Ship Electronic Engineering, 2011, 31(9):3.DOI:10.3969/j.issn.1627-9730.2011.09.034.
[2] Wu Xiaolin and Zhang Dong. Digital image edge detection based on genetic algorithm [J]. Ship Electronic Engineering, 2011, 031(009):117-118,134.
[3] Zhu Guowuzhuang Jinlei Wang Lichao Liu Bingyou. Research on image edge detection based on genetic algorithm [J]. Journal of Mudanjiang Normal University: Natural Science Edition, 2022(4):18-21.
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1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics site selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV application
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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