The project contains several small projects which will be used as a tutorial for Digital Image Processors. All the tasks must be completed at the lowest level of MATLAB code possible, with explanations line-by-line of the code. Many example codes will be provided as demonstrations of the type of code that needs to be written. The following must be used in the projects: loops logical if open/close/display properties of images histogram properties of images Project 1: Resample an image 5 times. This must be completed without using simple commands like 'resize' or 'reshape'. Display the resampled images inside the original image (picture in picture in picture etc etc...). Project 2: Threshold an image and various levels. Create a binary image. Display the various images. Project 3: Computer the negative of a given color image. Process the image using the Sobel gradients: |G|_K=|G_x|+|G_y|, K=R,G,B of each color component of the image. Add 1/10 of the Sorbel gradient to the color image. Display the images. Then, process the image using Robert gradients. Display the images. Project 4: Encode a message into a given image. Calculate the entropy and number of bits for encoding the message using Optimal, Huffman, and Arithmetic encoding. Generate 20 random pixels (x_n,y_n), n=1:20, in the area of size 200x200 with the center at (128,128). Hide the encoded message in the last insignificant bit-plane of the image at pixels which start from the generated pixels (x_n,y_n), n=1:20. Repeat for bit-plane 5. Display the results. Project 5: Read in a given image. Read in a target image. Rotate or flip the image as t(n,m)=t(N-n+1,M-m+1). Compose a new target image t*h_1 by a given 3x3 edge detector. Perform a windowed filter in the spatial domain. Clamp negative values to 0 for plotting. Pad the original and the contour target images to make them to be the size of 1024x1024. Perform the 2-D DFT's over the new images (normalize both, i.e. divide by 1024). Display the Fourier transforms as image(fftshift(abs(2-d DFT of the image)));colormap(gray(64));image(fftshift(abs(2-D DFT of the target))*16);. Computer the correlation in the Fourier domain and display it by "image(Correlation*64);colormap(gray(64))". Computer the histogram of the modified correlation function H -> H*(1024)^2. Print the coordinate of the first few maximums of the correlation function, for instance by using the threshold T=1.5. Mark the coordinates of centers of the target-images on the original image. Project 6: Process a given image degraded by smooth filer shifted at point (128,128) plus random noise n from a given .raw file. i(n,m)=(o*h)(n,m)+n(n,m), n,m=0:(N-1), N=256 Computer the transfer functions of the smooth H(w) and Wiener filters, Y_w(w), w=(w_1,w_2). Apply the Wiener filter to the DFT of the degraded image, which is O(w)H(w)+N(w). Compute errors of blurring, degradation, and filtration using a given code. Display the results. Display on another figure two meshes 20log|H_w(w_1,w_2)| and 20log|Y_w(w_1,w_2)+0.0001| using subplot(2,1,k) and axis([0,300,0,-400,0]) for k=1, and axis([0,300,0,-200,100]) for k=2. Use a given code to computer the errors and [EME]'s. Project 7: Process the given image from Project 6 using the same smoothing operation as in Project 6, h(n,m). Generate two normal distribution noise-images, n1(n,m) and n2(n,m), with mean 0 and standard deviation 4. Add these noise-images to the blur image as: i_k(n,m)=(o*h)(n,m)+n_k(n,m), n,m=0:255, k=1,2. Design the Wiener filter to process these two degraded images, and find the error of estimation of the original image, o(n,m). Display the results. Design the Wiener filter to process the image. Calculate the error of estimation of the image o(n,m). Display the results. All projects and images will be explained in full and given. Project should be completed in 3 weeks. Additional compensation may be provided for expedited services. Enjoy! Keywords: Mathematic, Engineering, Electrical, Algorithm, Mathematica, Matlab