【图像处理】图像配准、图像增强和图像分割研究（Matlab代码实现）

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📋📋📋本文目录如下：🎁🎁🎁

目录

💥1 概述

📚2 运行结果

2.1 图像分割

2.2 图像增强

2.3 图像配准

🎉3 参考文献

🌈4 Matlab代码实现

💥1 概述

图像处理领域涉及到许多重要的技术，其中包括图像配准、图像增强和图像分割。这些技术在不同的图像应用中发挥着重要的作用。

图像配准是指将多幅图像在空间上进行对齐，使得它们在几何、形状和灰度上具有一致性。常见的图像配准方法包括基于特征点的配准和基于互信息的配准。图像配准在医学影像、遥感影像等领域中广泛应用，用于将不同视角或不同时间拍摄的图像进行对比和分析。

图像增强是改善图像质量和提高图像视觉效果的过程。通过调整图像的亮度、对比度、锐化等参数，可以增强图像的细节并改善图像的可视化效果。图像增强在计算机视觉、数字摄影和图像分析等应用中具有重要意义，例如在低光照条件下提高图像可见度、减少噪声等。

图像分割指的是将图像划分为不同的区域或对象的过程。图像分割可以通过基于阈值、边缘检测、区域生长等方法实现。它在目标检测、图像识别和计算机辅助诊断等领域中具有广泛应用，可以帮助提取图像中的目标或感兴趣的区域。

综上，图像配准、图像增强和图像分割是图像处理领域中的重要研究方向。它们在各种图像应用中发挥着重要的作用，帮助我们更好地理解、分析和利用图像信息。

📚2 运行结果

2.1 图像分割

2.2 图像增强

2.3 图像配准

部分代码：

%% View the images side by side in a montage
imshowpair(Fixed,Moving,'montage');

%% Configure parameters in imregconfig
[optimizer,metric] = imregconfig('Multimodal');

%% Default registration
registered = imregister(Moving,Fixed,'translation',optimizer,metric);
figure;
imshowpair(registered,Fixed);
title('falsecolor');

%% Change visualization in imshowpair
figure;
imshowpair(registered,Fixed,'blend');

%% Change transformType in imregister
registered = imregister(Moving, Fixed,'affine',optimizer,metric);
figure;
imshowpair(registered,Fixed);
title('Intermediate Registration');

%% Final registration
registered = imregister(Moving, Fixed,'Similarity',optimizer,metric);
figure;
imshowpair(registered,Fixed);title('Final Registration');

%% Detect the eyes in the RGB image
eyesDet = vision.CascadeObjectDetector('EyePairSmall');
bbox = step(eyesDet, Moving);
drawBox = vision.ShapeInserter('BorderColor','Black');
image = step(drawBox, registered, int32(bbox));
hold on; rectangle('Position',bbox,'EdgeColor',[1 1 0]);
subsIR = int32(bbox(:,1:2)+bbox(:,3:4)/2);

%% Compute temperature near the eyes
value = mean2(imcrop(registered,bbox));
foreheadTemperature = value/10 - 272; % In Celcius
foreheadTemperature =  (foreheadTemperature*9/5) + 32; % Convert to Farenheit

%% Embed temperature on IR image and display
ti = vision.TextInserter('Color',[255 0 0]);
ti.Location = int32(bbox(:,1:2)+bbox(:,3:4)/2);
ti.Text = sprintf('%3d F', int8(foreheadTemperature));
contAdj = vision.ContrastAdjuster('CustomProductInputDataType',numerictype([],32,8));
imageContrastAdjusted = step(contAdj, Fixed);
textAdded = step(ti, imageContrastAdjusted);
text(320, 180,'98 \circ F ','Color',[1 1 0])

%% View the images side by side in a montage
imshowpair(Fixed,Moving,'montage');

%% Configure parameters in imregconfig
[optimizer,metric] = imregconfig('Multimodal');

%% Default registration
registered = imregister(Moving,Fixed,'translation',optimizer,metric);
figure;
imshowpair(registered,Fixed);
title('falsecolor');

%% Change visualization in imshowpair
figure;
imshowpair(registered,Fixed,'blend');

%% Change transformType in imregister
registered = imregister(Moving, Fixed,'affine',optimizer,metric);
figure;
imshowpair(registered,Fixed);
title('Intermediate Registration');

%% Final registration
registered = imregister(Moving, Fixed,'Similarity',optimizer,metric);
figure;
imshowpair(registered,Fixed);title('Final Registration');

%% Detect the eyes in the RGB image
eyesDet = vision.CascadeObjectDetector('EyePairSmall');
bbox = step(eyesDet, Moving);
drawBox = vision.ShapeInserter('BorderColor','Black');
image = step(drawBox, registered, int32(bbox));
hold on; rectangle('Position',bbox,'EdgeColor',[1 1 0]);
subsIR = int32(bbox(:,1:2)+bbox(:,3:4)/2);

%% Compute temperature near the eyes
value = mean2(imcrop(registered,bbox));
foreheadTemperature = value/10 - 272; % In Celcius
foreheadTemperature = (foreheadTemperature*9/5) + 32; % Convert to Farenheit

%% Embed temperature on IR image and display
ti = vision.TextInserter('Color',[255 0 0]);
ti.Location = int32(bbox(:,1:2)+bbox(:,3:4)/2);
ti.Text = sprintf('%3d F', int8(foreheadTemperature));
contAdj = vision.ContrastAdjuster('CustomProductInputDataType',numerictype([],32,8));
imageContrastAdjusted = step(contAdj, Fixed);
textAdded = step(ti, imageContrastAdjusted);
text(320, 180,'98 \circ F ','Color',[1 1 0])