Digital Imaging MATHLAB

Using MATLAB need to write code to perform the following: 1. Read in the colour image [login to view URL] and display the image. Convert the image to gray-scale using the function rgb2gray. Display the gray-scale image and save it as gstestimage1a.bmp. Apply a threshold to the gray-scale image to form a binary image. The user should be allowed to change the threshold. To achieve the binary image the program should determine whether the intensity of each pixel is above the threshold; the program should use „for‟ or „while‟ loops. Do not use im2bw or change the colormap. Display the binary image using colormap(gray(256)). Calculate the number of pixels with intensity above, below and equal to the threshold and display the results. Obtain results using three different threshold values 75, 115 and 210. Comment on the results. 2. Calculate the mean, standard deviation, minimum and maximum of the intensity of the gray scale image [login to view URL] obtained above from the image testimage1a.bmp. Reduce the image width and height to 0.2 of the original using nearest neighbour and bilinear interpolation (Hint: use imresize ) and then perform another resize to enlarge the reduced images to the original size. Display the images before and after each resizing. Now calculate the mean, standard deviation, minimum and maximum of the intensity in the resultant same sized image. Compare and comment on the results before and after resizing and with the two different methods of resizing. 3. Add noise to the gray scale image gstestimage1a.bmp. (Hint: use imnoise and choose the 'salt & pepper' option). Apply median filters with 5x5 and 11x11 neighbourhood sizes to the image with added noise and to the image gstestimage1a.bmp. (Hint: use medfilt2 ). Comment on the resultant images. Read in the colour image [login to view URL] and then save the colour image as jpg files in „lossy‟ mode with three different quality values 15, 60 and 98. Display the three jpg files and comment on them and the file sizes.