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DSIP Exp 6

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  Subject: DSIP   Class:   BE COMPS A.Y. 2019-20EXPERIMENT 6Problem Statement: To implement Image negative, contrast stretching and thresholding. Theory: POINT OPERATIONS: Point operations translate gray scale values on a pixel-by-pixel basis from one image to another. Thegoal of histogram equalization is to find and apply a point operation such that the histogram of themodified image approximates a uniform distribution. Histogram is a discrete distribution &homogeneous point operations can only shift & merge(but never split) histogram entries, we onlyobtain approximate solution in general.Zero memory operations where a given gray level u [0, L] is mapped into a gray level v [0, L] according to ∈ ∈  a transformation  v = f(u)  Input and output gray levels are distributed between [0, L].Typically, L = 255 Image Negative: The negative of an image with grey levels in the range [0, L-1] is obtained by the negativetransformation shown in figure above, which is given by the expression as  s = L – 1 – r  . Thisexpression results in reversing of the gray level intensities of the image thereby producing a negativelike image. In negative transformation, each value of the input image is subtracted from the L-1 andmapped onto the output image. This is particularly useful for enhancing white or gray detailsembedded in dark regions of an image. Contrast Stretching: Often called as Normalisation   is a simple image enhancement technique thatattempts to improve the contrast  in an image by ` stretching ' the range of intensity values it containsto span a desired range of values, e.g. the the full range of pixel values that the image type concernedallows Thresholding: Thresholding is the special case of clipping where output becomes binary.Image Procesing (BE COMPS)  Code: Image Procesing (BE COMPS)  PROGRAM : (CONTRAST STRETCHING) #include <iostream>#include <opencv2/core/core.hpp>#include<opencv2/highgui/highgui.hpp>using namespace cv;using namespace std;int computeOutput(int , int , int , int ,int);int main(){Mat image = imread( image.jpeg );Mat new_image = image.clone();int r1,s1,r2,s2;cout<< Enter r1 : <<endl;cin>>r1;cout<< Enter s1 : <<endl;cin>>s1;cout<< Enter r2 : <<endl;cin>>r2;cout<< Enter s2 : <<endl;cin>>s2;for(int y=0;y<image.rows;y++){for(int x=0;x<image.cols;x++){for(int c=0;c<3;c++){int output = computeOutput(imageat<Vec3b>(y,x)[c],r1,s1.r2,s2);new_image.at<Vec3b>(y,c)[c] = saturate_ucast<uchar>(output);}}}namedWindow( Original Image ,1);imshow( Original Image ,image);namedWindow( Modified Image ,1);imshow( Modified Image ,new_image);waitKey();return 0;}int computeOutput(int x, int r1 , int s1 , int r2 ,int s2){float result;if(0<=x&& x<=r1){result = s1/r1*x;}elseif(r1<x & x<=r2){result=((s2-s1)/(r2-r1)*(x-r1)+s1;}elseif(r2<x && x<=255){result = ((255-s2)/(255-r2))*x-r2+s2;}return (int)result;Image Procesing (BE COMPS)  } Note: Write your own code Digital Negative and Contrast StretchingConclusion: Single-point processing is a simple method of image enhancement. This techniquedetermines a pixel value in the enhanced image dependent only on the value of the correspondingpixel in the input image. Image Procesing (BE COMPS)

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Sep 22, 2019

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Sep 22, 2019
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