Path to mathematics-python computing practice (11)-machine vision-Image Enhancement-python practice

Path to mathematics-python computing practice (11)-machine vision-Image Enhancement-python practice

In the computer field, Gray scale digital images are images with only one sampling color per pixel. This type of image is usually displayed as a gray scale from the shortest black to the brightest white, although theoretically this sample can be of different colors or even different colors on different brightness. Gray images are different from black and white images. In computer images, black and white images only have two colors. gray images have many levels of color depth between black and white. The gray scale images used for display are usually stored at a non-linear scale of 8 bits per sample pixel, which can have 256 kinds of gray scale (8bits is the 8th power of 2 = 256 ). This accuracy just avoids visible band distortion and is very easy to program. A grayscale image is a monochrome image with a gray level or level from black to white. Each pixel in the image is represented by 8 bits, so the pixel value is between one of the 256 gray levels in black and white. This image only has a gray level without color changes.

Grayscale transformation is an enhancement method based on vertex operations. It converts the gray value of each pixel into a new gray value according to a certain mathematical transformation formula, which can enhance the image and expand the image contrast, makes the image clearer and highlights its features.

Gray-scale nonlinear transformation refers to converting gray-scale data based on empirical data or some arithmetic nonlinear relationship before display. gray-scale transformation is an enhancement method based on point operations, it converts the gray value of each pixel into a new gray value according to a certain mathematical transformation formula, such as contrast enhancement in enhancement processing.

The logarithm transform greatly expands the low brightness area of the image and compresses the High Brightness area. In short, it enhances the image details of the low gray level. The gray level nonlinear transformation formula is as follows:

Dst = C * log (1 + src)

The picture on the left is operated by nonlinear transformation.

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import cv2import numpy as npfn="test3.jpg"myimg=cv2.imread(fn)img=cv2.cvtColor(myimg,cv2.COLOR_BGR2GRAY)  jg_img=np.array(40*np.log(img+1),np.uint8)cv2.imshow('src',img)cv2.imshow('dst',jg_img)cv2.waitKey()cv2.destroyAllWindows()

Has anyone ever used mathematical image processing and Machine Vision Visual C ++ and matlab? The program result in Chapter 1 is incorrect.

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Translation of English literature and Chinese for Digital Image Processing

Lecture 1-Overview
HD Cheng, XH Jiang, Y Sun, Jingli Wang. Color image segmentation: advances and prospects. Pattern Recognition, Vol. 34, No. 12. pp. 2259-2281, Dec. 2001
Lecture 2-spatial domain image enhancement
C. W. Nog, T. C. Pong, and R. T. Chin. Video Partition by Temporal Slice Coherency. IEEE Transactions on Circuits and Systems for Video Technology, Aug. 2001.
Lecture 3-Color Image Processing
Smith, A.R. Color Gamut Transform Pairs. Proc. SIGGRAPH '78, published as Computer Graphics, vol. 12, no. 3, pp. 12-19,197 8.
MacAdam, D.L. Visual Sensitivities to Color Differences in Daylight. J. Opt. Soc. Am., vol. 32, pp. 247-274,194 2.
Liu, J., and Yang, Y.-H. multiresolution color image segmentation. IEEE Trans Pattern Anal. Machine Intell., vol. 26, no. 5, pp. 647-661,199 4.
Shafarenko, L ., petrou, M ., and Kittler, J. histogram-based segmentation in a perceptually uniform color space. IEEE Trans. image Processing, vol. 7, no. 9, pp. 1354-1358,199 8.
Lecture 4-content-based image retrieval
Y. rubner, C. tomasi and L. j. guibas. the Earth Mover's Distance as a Metric for Image Retrieval. international Journal of Computer Vision 40 (2), 99-121,200 0.
J.-L. Shih and L.-H. Chen Colour image retrieval based on primitives of color moments. IEEE Proc. Vision Image Signal Process, 2002.
B .S. manjunath and W. y. ma. texture Feature for Browsing and Retrieval of Image Data. IEEE Transaction on Pattern Analysis and Machine Intelligence ., vol. 18, no. 8, August 1996.
N. Dalal and Bill Triggs. Histograms of Oriented Gradients for Human Detection. IEEE Computer So ...... the remaining full text>