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# point operations in image processing

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### point operations in image processing

1. 1. Point Operations in Image Processing<br />R.Logarajah(2005/SP/30)<br />Department of Computer Science, University of Jaffna<br />Histogram Equalization<br />Brightness<br />Introduction<br /><ul><li>Point operation perform a modification of the pixel values without changing the size, geometry, or local structure of the image. Each new pixel value a′=I′(x,y) depends exclusively on previous value a=I(x,y) at the same position and is independent from any other pixel value in particular from any of its neighboring pixels.
2. 2. Point operations translate gray scale values on a pixel-by-pixel basis from one image to another.
3. 3. Objective:-The goal of histogram equalization is to find and apply a point operation such that the histogram of the modified image approximates a uniform distribution.
4. 4. Histogram is a discrete distribution & homogeneous point operations can only shift & merge(but never split) histogram entries, we only obtain approximate solution in general.
5. 5. Brightness is to add a constant amount of light to the sample value in each position in the image raster.
6. 6. Objective:-increase brightness(for 256 grayscale image) .
7. 7. A Solution:- fbright(a) = min(a +Shift value, 255) .</li></ul>Point Operation<br />Output pixels are a function of only one point:<br /> G(x,y)=T[f(x,y)]<br />Inverting<br />Histogram Equalization: Cumulative Histograms<br /><ul><li>A Solution:-</li></ul>Here, An image of size M X N<br />With pixels values a in the range [0,k-1].<br />M.N-total number pixels.<br />a pixel<br />a pixel<br />Input: f(x,y)<br />Output:G(x,y)<br /><ul><li>Inverting the sample values in the image, produces the same image that would be found in a photo negative.
8. 8. Objective :-inverse image (for 256 grayscale image).
9. 9. A solution finverse(a) = 255 − a</li></ul>Methods<br /><ul><li>Original images are converted to the gray level an images.
10. 10. Contrast modifications, logical operations and threshold are examples of this kind of operation.
11. 11. Gray level images store with 8 bits which allows 256. </li></ul>Threshold<br />Alpha blending <br />Results<br />Note:-<br /><ul><li>Here a is pixel value within original image.
12. 12. In every operation first and second images are original and histogram.
13. 13. The following third and fourth figures are modified image and it’s histogram respectively, which have been operated.
14. 14. Threshold an image is the process of making all pixels above a certain threshold level white, others black.
15. 15. Objective :-select pixels within boundaries(for 256 grayscale image).
16. 16. A solution:-</li></ul>α=0<br />α=1<br />α=0.3<br />Alpha blending is a simple method for transparently overlaying two images. Transparency control is a α. Here α is range 0≤α≤1; for α=0 the foreground image(Ifg) and α=1 the background image (Ibg) are visible.<br />A Solution:-I’(x,y)= α.Ibg(x,y)+(1- α).Ifg(x,y). <br /> with parameter α∈ [0;1](for grayscale images).<br />Automatic Contrast Adjustment<br />Contrast<br />Conclusion<br />In this paper we propose a point operation use to spatial images. We achieve, the ability to learn the specified field contrast, brightness, inverting, threshold and histogram of images.<br /><ul><li>Objective:- Complete the range of available values(for grayscale images) range =>0 to 255.
17. 17. Assume that alow & ahigh are the lowest &highest pixel values. found in the current image, whose full intensity rang is [amin,amax].
18. 18. A Solution:-</li></ul>Reference<br />W.Borge,Principles of Digital Image Processing, Undergraduate Topics in Computer<br />Science.DOI 10.1007/978-1-84800-191-6-6© Sprinner-Verlea London Limited 2000.<br /><ul><li>Objective:- increase contrast (for 256 grayscale image).
19. 19. A solution :-fcontrast(a) =min{a×1.5 , 255} </li>