1. This problem investigates nearest neighbor and bilinear interpolation. For si

Question

1. This problem investigates nearest neighbor and bilinear interpolation. For simplicity, we will focus on estimating the image intensity at a single location. Interpolation is used when transforming an image through resizing, rotating, etc. in which case, the image intensity will need to be estimated at a number of locations Consider the diagram below of four pixels 2 where the dots () represent the locations where we know the image intensity and the × represents the location where we would like to estimate the image intensity. By convention, the vertical axis is the x-axis and the horizontal axis is the y-axis. Suppose the four pixels are at the following locations (indicated by (xy)) and have the following intensity values (indicated by p) (x1,y1)= (4,10) (X2,y2) = (4,11) (x3Js) = (5,10) (x4.34)= (5,11) p1=100 p2-107 p3-120 p4-130 and that we would like to estimate the image intensity at a fifth location (x5,Ys) = (4.3, 10.4) That is, we want to estimate ps a) Provide an estimate for ps using nearest neighbor interpolation.

Explanation / Answer

% scale up using interpolation

img = imread('t1.gif');

prompt = 'Enter the scale up value ';

zoom_scale = input(prompt);

a = size(img);

x = a(:,1);

y = a(:,2);

%z = a(:,3);

for i = 1:zoom_scale*x

xx = i/zoom_scale;

x1 = max(1,floor(xx));

x2 = ceil(xx);

x_int = rem(xx,1);

for j = 1:zoom_scale*y

yy = j/zoom_scale;

y1 = max(1,floor(yy));

y2 = ceil(yy);

y_int = rem(yy,1);

BL = img(x1,y1,:);

TL = img(x1,y2,:);

BR = img(x2,y1,:);

TR = img(x2,y2,:);

R1 = BR*y_int+BL*(1-y_int);

R2 = TR*y_int+TL*(1-y_int);

res_img(i,j,:) = R1*x_int+R2*(1-x_int);

end

end

I = mat2gray(res_img);

imshow(I);

Related Questions

Navigate

• Browse (All)
• Browse 1
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.