write MATLAB scripts to apply some important impage processing alogrithms to a set of test images.

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Computing 1: ELE1053 Project 4E: Image Processing Project Principle Objective: Write MATLAB scripts to apply some important image processing algorithms to a set of test images. Implementation: MatLab has a rich and extensive library of functions that can be used by scientists and engineers to analyze image data. The algorithms for these functions are included in one of then MatLab's toolboxes. In this project you will creates MatLab scripts that can apply some key processing algorithms to a set of test images. For the given set of test images you will apply the following important Image Processing algorithms. Image scaling Colour adjustment Edge detection Convolution image filtering) This project consists of two parts: Part 1: Follow the instructions below and where asked apply them to test image 1. Step1: Reading and writing images in Matlab using different image file formats. img-imread('1.jpg') dim-size (img) make sure you get the correct path note the size of the image imshow (img): I-img; make copy of image imwrite(ing, 'output.bmp', 'bmp'); save a copy of the image Find the original file and the saved file, compare their file sizes. Step2: Create an image consisting entirely of noise row - 256; col = 256; img-rand (row, col); round (img); imshow (img); What is the size of the image? Save your image to a file. Step3: Changing an image size: A imread('1.jpg'); imshow (A); B - imresize(A, [128, 128]); imshow (B): Has this changed the shape of the image as well as its size? Step 4: Cropping an image: A = imread ('1.jpg'); imshow (A); rect=132 32 64 64); imcrop (A, rect); imshow (B); B What is the new size of the image? A Step 5: Converting Colour image to Grayscale: imread('1.jpg'); imshow (A); B - rgb2gray (A); imshow (B); Step5: Making a blurred image by using a Gaussian Convolution I - imread('1.jpg"); PSF - Especial('gaussian', 32, 32); Blurred - imfilter (I, PSF, 'conv'); imshow (Blurred); Try different values of the size of the Point Spread Function (PSF) to increase the degree of blurring. Step6: Adding noise to the blurred image. V - .02; BlurredNoisy - imnoise (Blurred, 'Gaussian',0,1); 3 - Figure; imshow (BlurredNoisy); Step 7: Applying Edge Detection to the original Image A = imread('1.jpg'); B = rgb2gray(A); $ Use the 'canny' edge detection algorithm E=edge (B,'canny'); imshow (E); % Use the 'sobel' edge detection algorithm E-edge (B,' sobel'); imshow (E); Note whether you see any differences in the two algorithms: Step8: Create a Negative of the image: A = imread ('1.jpg'); B = imcomplement (A); &show the two images side by side imshowpair (A,B,' montage') Part 2: Apply the following algorithms to test image 2-8. You may need to search on- line or in the MatLab help file to find details of how to use these algorithms, but for most of the steps the MatLab code has been used in Part 1. For each case, read the image from its file. Apply each algorithm in turn and store the final image in an image file of your choice. Also create script files for each of your processes. 1. Resize the image to a size of 512 by 512 pixels 2. Rotate it by 180 degrees. 3. Apply a Gaussian smoothing filter to the image, use your judgement to decide on the degree of blurring so that the image does not look too blurry, 4. Create a monochrome version of the image. 5. Make a negative of the image. 6. Apply an edge detection algorithm. Use your judgement to decide which of the available detection algorithms gives the best results. 7. Complete the result of applying the edge detection algorithm to the algorithm with and without the smoothing filter applied