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Content introduction
In the field of digital image processing, we often need to perform various processing operations on images to obtain the visual effects we require. Some common operations include color mapping, resizing and changing resolution, gamma correction, contrast adjustment, and color inversion. This article will delve into the principles, applications, and effects of these operations.
First, let’s understand color mapping. Color mapping is the process of mapping an image from one color space to another. Common color spaces include RGB (red, green, blue), CMYK (cyan, yellow, magenta, black), and HSV (hue, saturation, brightness). Through color mapping, we can change the hue, saturation and brightness of the image to achieve different visual effects. For example, converting a color image to a black and white image is a color mapping operation.
Next, we’ll discuss resizing and changing resolution. Resizing refers to changing the dimensions of an image, usually by increasing or decreasing the number of pixels in the image. This can be used to resize the image on different display devices, or to create thumbnails. Changing the resolution refers to changing the pixel density of the image, which is the number of pixels per inch. By increasing or decreasing pixel density, we can change the clarity and level of detail in an image.
Gamma correction is an operation used to adjust the brightness and contrast of an image. It is based on the way the human eye perceives brightness and changes the overall brightness of the image by adjusting the brightness curve of the image. Gamma correction can be used to increase the contrast of an image, making it more vivid and clear.
Contrast adjustment is an operation that changes the difference between brightness levels in an image. By increasing or decreasing the difference between brightness levels, we can change the overall contrast of the image. This is useful for highlighting details in an image or creating an artistic effect.
Finally, we’ll discuss color inversion. Color inversion is an operation that inverts the color values in an image. This means that bright pixels become darker, dark pixels become lighter, red becomes cyan, green becomes magenta, and blue becomes yellow. Color inversion can be used to create artistic effects or enhance specific areas in an image.
To summarize, various color mapping, resizing and changing resolution, gamma correction, contrast adjustment, and color inversion operations are all common operations in digital image processing. They can be used to change the appearance and visual effects of an image, making it more vivid, clear and attractive. Whether in artistic creation or scientific research, these operations play an important role. By in-depth understanding of the principles and applications of these operations, we can better utilize digital image processing technology to create stunning image works.
Part of the code
m=1;
for i=0:pi/180:2*pi
if(p==1)
D2(m)=sqrt((P(2,1)-d(1)*cos(i))^2 + (P(2,2)-d(1)*sin(i))^2);
D3(m)=sqrt((P(3,1)-d(1)*cos(i))^2 + (P(3,2)-d(1)*sin(i))^2);
e2(m)=abs(D2(m)-d(2));
e3(m)=abs(D3(m)-d(3));
subplot(3,1,1);
plot(m,D2(m),'k.');
hold on
grid on
xlabel('angle in degrees')
ylabel('distance')
title('variation in distance with angle')
subplot(3,1,2);
plot(m,D3(m),'b.');
xlabel('angle in degrees')
ylabel('distance')
title('variation in distance with angle')
hold on
grid on
elseif(p==2)
D1(m)=sqrt((P(1,1)-d(1)*cos(i))^2 + (P(1,2)-d(1)*sin(i))^2);
D3(m)=sqrt((P(3,1)-d(1)*cos(i))^2 + (P(3,2)-d(1)*sin(i))^2);
e1(m)=abs(D1(m)-d(1));
e3(m)=abs(D3(m)-d(3));
subplot(3,1,1);
plot(m,D1(m),'k.');
hold on
grid on
xlabel('angle in degrees')
ylabel('distance')
title('variation in distance with angle')
subplot(3,1,2);
plot(m,D3(m),'b.');
hold on
grid on
xlabel('angle in degrees')
ylabel('distance')
title('variation in distance with angle')
else
D1(m)=sqrt((P(1,1)-d(1)*cos(i))^2 + (P(1,2)-d(1)*sin(i))^2);
D2(m)=sqrt((P(2,1)-d(1)*cos(i))^2 + (P(2,2)-d(1)*sin(i))^2);
e1(m)=abs(D1(m)-d(1));
e2(m)=abs(D2(m)-d(2));
subplot(3,1,1);
plot(m,D1(m),'k.');
hold on
grid on
xlabel('angle in degrees')
ylabel('distance')
title('variation in distance with angle')
subplot(3,1,2);
plot(m,D2(m),'b.');
hold on
grid on
xlabel('angle in degrees')
ylabel('distance')
title('variation in distance with angle')
end
m=m + 1;
end
Run results
References
[1] Kang Yanni, Huang Huan, Zhu Yuyan, et al. SIFT-based super-resolution image registration and MATLAB implementation [J]. Computer Knowledge and Technology: Academic Edition, 2009, 5(10):3.DOI:10.3969/ j.issn.1009-3044.2009.28.079.
[2] Zhao Hongyan, Tang Wanyou, Tan Huan. Research on the edge detection effect of images with different resolutions based on MATLAB [J]. Packaging Engineering, 2009(11):4.DOI:CNKI:SUN:BZGC.0.2009-11 -028.
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