History
editThe cost of processing was fairly high, however, with the computing equipment of that era. That changed in the 1970s, when digital image processing proliferated as cheaper computers and dedicated hardware became available. This led to images being processed in real-time, for some dedicated problems such as television standards conversion. As general-purpose computers became faster, they started to take over the role of dedicated hardware for all but the most specialized and computer-intensive operations. With the fast computers and signal processors available in the 2000s, digital image processing has become the most common form of image processing, and is generally used because it is not only the most versatile method, but also the cheapest.
Image sensors
editImage compression
editMedical imaging techniques produce very large amounts of data, especially from CT, MRI and PET modalities. As a result, storage and communication of electronic image data are prohibitive without the use of compression. JPEG 2000 image compression is used by the DICOM standard for storage and transmission of medical images. The cost and feasibility of accessing large image data sets over low or various bandwidths are further addressed by use of another DICOM standard, called JPIP, to enable efficient streaming of the JPEG 2000 compressed image data.
Digital signal processor (DSP)
edit
Affine transformations
editAffine transformations enable basic image transformations including scale, rotate, translate, mirror and shear as is shown in the following examples:
| Transformation Name | Affine Matrix | Example | |
|---|---|---|---|
| Identity | [cx=2000cy=10001] |  | |
| Rotate | y are the row and column of a pixel in the image matrix. This allows the coordinate to be multiplied by an affine-transformation matrix, which gives the position that the pixel value will be copied to in the output image.
However, to allow transformations that require translation transformations, 3-dimensional homogeneous coordinates are needed. The third dimension is usually set to a non-zero constant, usually 1, allows translation. Because matrix multiplication is associative, multiple affine transformations can be combined into a single affine transformation by multiplying the matrix of each individual transformation in the order that the transformations are done. This results in a single matrix that, when applied to a point vector, gives the same result as all the individual transformations performed on the vector (0, 0), then perform the rotation, and lastly translate the origin B=[121211103],B:𝒮→ℝ,𝒮={−1,0,1}2. Here, B(m,n) bias the image during dilation and erosion.
(f⊕B)(1,1)=max(f(0,0)+B(−1,−1),45+1;f(1,0)+B(0,−1),50+2;f(2,0)+B(1,−1),65+1;f(0,1)+B(−1,0),40+2;f(1,1)+B(0,0),60+1;f(2,1)+B(1,0),55+1;f(0,2)+B(−1,1),25+1;f(1,2)+B(0,1),15+0;f(2,2)+B(1,1)5+3)=66.
(f⊖B)(1,1)=min(f(0,0)−B(−1,−1),45−1;f(1,0)−B(0,−1),50−2;f(2,0)−B(1,−1),65−1;f(0,1)−B(−1,0),40−2;f(1,1)−B(0,0),60−1;f(2,1)−B(1,0),55−1;f(0,2)−B(−1,1),25−1;f(1,2)−B(0,1),15−0;f(2,2)−B(1,1)5−3)=2. ResultseditAfter applying dilation to f: B would first reduce small details (through erosion) and then restore the main shapes (through dilation). This ensures that unwanted noise is removed without significantly altering the size or shape of larger objects.
For instance, applying closing to the same image [111111111] |
Use Matlab to dilation
imwrite(image,'scene_ero.jpg');
extractmin=zeros(size(image),class(image));
fori=1:time
ero_image=imread('scene_ero.jpg');
forcol=(halfW+1):(c-halfW)
forrow=(halfH+1):(r-halfH)
pointDown=row-halfH;
pointUp=row+halfH;
pointLeft=col-halfW;
pointRight=col+halfW;
neighbor=ero_image(pointDown:pointUp,pointLeft:pointRight);
filter=neighbor(se);
extractmin(row,col)=min(filter);
end
end
imwrite(extractmin,"scene_ero.jpg");
end
|
 |
| Opening | [111111111] | Use Matlab to Closing
Digital camera imageseditDigital cameras generally include specialized digital image processing hardware – either dedicated chips or added circuitry on other chips – to convert the raw data from their image sensor into a color-corrected image in a standard image file format. Additional post-processing techniques increase edge sharpness or color saturation to create more naturally looking images. FilmeditWestworld (1973) was the first feature film to use digital image processing to pixellate photography to simulate an android's point of view. Image processing is also vastly used to produce the chroma key effect that replaces the background of actors with natural or artistic scenery. Face detectionedit Face detection process
Face detection can be implemented with mathematical morphology, the discrete cosine transform (DCT), and horizontal projection. General method with feature-based method The feature-based method of face detection uses skin tone, edge detection, face shape, and features of a face (like eyes, mouth, etc.) to achieve face detection. The skin tone, face shape, and all the unique elements that only the human face can be described as features. Process explanation
Improvement of image quality methodeditImage quality can be influenced by camera vibration, overexposure, gray level distribution being too centralized, and noise, etc. For example, the noise problem can be solved by smoothing, while the gray level distribution problem can be improved by histogram equalization. Smoothing method In drawing, if there is a dissatisfied color, taking some color around the dissatisfied color and averaging them. The smoothing method can be implemented with a mask and convolution. Take a small image and mask, for instance, as below. image is [25653910619927322] Observing image[1, 1], image[1, 2], image[2, 1], and image[2, 2]. The original image pixels are 1, 4, 28, 30. After the smoothing mask, the pixels become 9, 10, 9, 9, respectively. new image[1, 1] = 19 * (5+6+5+1+4+6+28+30+2)) = 10 new image[2, 1] = floor(∑i=0kH(pi)(see figure 1), while the area of the uniform distribution is qi is ∫q0qN2qk−q0ds=∫p0pH(s)ds. Moreover, based on the definition of a function, the Gray level histogram method is like finding a function
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