降低目标对比度而不丢失图像细节

Question

我有一个原始图像（左侧），我想降低对象（水平形状的东西）对比度以模拟参考图像（右侧）。我尝试了所有平滑方法，但它们都会影响图像对比度（比如背景对比度）。有什么方法可以建立模型然后从原始图像中减去它？

换句话说，我们有什么办法可以给左边的图像添加不透明度来模拟右边的图像？已提供对象蒙版，其大小与原始图像相同。

任何建议将不胜感激。

Answer 1

您可以通过乘以输入图像中的蒙版区域来降低对比度：

I = imread('https://i.stack.imgur.com/QML1p.png'); %I = imread('1.png'); %Read image
M = imbinarize(imread('https://i.stack.imgur.com/nBqeS.png')); %imbinarize(imread('2.png')); %Read mask, and convert to binary image.

J = I;
J(M) = J(M)*.5; %Multiply the "horizontal shape thing" by 0.5 - reducing contrast.

figure;imshow(J);

结果：

上述解决方案是设置“形状事物”的不透明度。

---

我试着让它更平滑一点，但结果还是很不一样。

I = imread('https://i.stack.imgur.com/QML1p.png'); 
%I = imread('1.png'); %Read image
M = imbinarize(imread('https://i.stack.imgur.com/nBqeS.png')); 
%M = imbinarize(imread('2.png')); %Read mask, and convert to binary image.

M = imdilate(M, strel('disk', 20)); %Make mask thicker.

%Crate blurred "shape thing"
T = zeros(size(I));
T(M) = double(I(M)); %"shape thing" with some margins, and black surrounding.
T = T/255; %Change pixels range from [0, 255] to [0, 1].
T = imgaussfilt(double(T), 5); %Blur (2-D Gaussian filtering).

%Create blurred mask:
M = imgaussfilt(double(M), 20); %Blur the mask (2-D Gaussian filtering).
M = max(M, 0.5);

J = T.*M + im2double(I).*(1-M); %Weighed average.
J = imgaussfilt(J, 1);
J = im2uint8(J);

figure;imshow(J);

结果：

试着玩一下...

---

再试一次：

• 通过平均 J 和 0.5 来降低“形状事物”与背景之间的相对对比度
• 添加高斯噪声（使结果更接近参考）。

代码：

I = imread('https://i.stack.imgur.com/QML1p.png'); 
M = imbinarize(imread('https://i.stack.imgur.com/nBqeS.png')); 

M = imdilate(M, strel('disk', 20)); %Make mask thicker.

%Crate blurred "shape thing"
T = zeros(size(I));
T(M) = double(I(M)); %"shape thing" with some margins, and black surrounding.
T = T/255; %Change pixels range from [0, 255] to [0, 1].
T = imgaussfilt(double(T), 5); %Blur (2-D Gaussian filtering).

T = T*1.5; %Scale T for changing opaquely

%Create blurred mask:
M = imgaussfilt(double(M), 20); %Blur the mask (2-D Gaussian filtering).
M = max(M, 0.5);

J = T.*M + im2double(I).*(1-M); %Weighed average.
J = imgaussfilt(J, 1);

%Reduce the relative contrast between "shape thing" and background by averaging J and 0.5
J = (J + 0.5)/2;
J = min(J, 1);

%Add gaussian noise (so result be closer to reference). 
J = imnoise(J, 'gaussian', 0, 0.005);

J = im2uint8(J);

figure;imshow(J);

结果：