# Lab P-8: Digital Images: A/D and D/A

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2 This is because light images are formed by measuring the intensity of reflected or emitted light which must always be a positive finite quantity. When stored in a computer or displayed on a monitor, the values of xœm;n have to be scaled relative to a maximum value X max. Usually an eight-bit integer representation is used. With 8-bit integers, the maximum value (in the computer) would be X max D D 255, and there would be 2 8 D 256 different gray levels for the display, from 0 to Displaying Images As you will discover, the correct display of an image on a gray-scale monitor can be tricky, especially after some processing has been performed on the image. We have provided the function show img.m in the DSP First Toolbox to handle most of these problems, 3 but it will be helpful if the following points are noted: 1. All image values must be nonnegative for the purposes of display. Filtering may introduce negative values, especially if differencing is used (e.g., a high-pass filter). 2. The default format for most gray-scale displays is eight bits, so the pixel values xœm;n in the image must be converted to integers in the range 0 xœm;n 255 D The actual display on the monitor is created with the show img function. 4 The show img function will handle the color map and the true size of the image. The appearance of the image can be altered by running the pixel values through a color map. In our case, we want grayscale display where all three primary colors (red, green and blue, or RGB) are used equally, creating what is called a gray map. In MATLAB the gray color map is set up via colormap(gray(256)) which gives a matrix where all 3 columns are equal. The function colormap(gray(256)) creates a linear mapping, so that each input pixel amplitude is rendered with a screen intensity proportional to its value (assuming the monitor is calibrated). For our lab experiments, non-linear color mappings would introduce an extra level of complication, so they will not be used. 4. When the image values lie outside the range [0,255], or when the image is scaled so that it only occupies a small portion of the range [0,255], the display may have poor quality. In this lab, we will use show img.m to automatically rescale the image: This requires a linear mapping of the pixel values: x s Œm;n D xœm;n C ˇ The scaling constants and ˇ can be derived from the min and max values of the image, so that all pixel values are recomputed via: 5 xœm;n x s Œm;n D 255:999 x max xmin x min where bxc is the floor function, i.e., the greatest integer less than or equal to x. 3 If you have the MATLAB Image Processing Toolbox, then the function imshow.m can be used instead. 4 If the MATLAB function imagesc.m is used to display the image, two features will be missing: (1) the color map may be incorrect because it will not default to gray, and (2) the size of the image will not be a true pixel-for-pixel rendition of the image on the computer screen. 5 The MATLAB function show img has an option to perform this scaling while making the image display. 2 McClellan, Schafer and Yoder, Signal Processing First.

4 2 Warm-up The instructor verification sheet may be found at the end of this lab. 2.1 Synthesize a Test Image In order to probe your understanding of the relationship between MATLAB matrices and image display, you can generate a synthetic image from a mathematical formula. (a) Generate a simple test image in which all of the columns are identical by using the following outer product: xpix = ones(256,1)*cos(2*pi*(0:255)/16); Display the image and explain the gray-scale pattern that you see. How wide are the bands in number of pixels? How can you predict that width from the formula for xpix? (b) In the previous part, which data value in xpix is represented by white? which one by black? (c) Explain how you would produce an image with bands that are horizontal. Give the formula that would create a image with 5 horizontal black bands separated by white bands. Write the MATLAB code to make this image and display it. Instructor Verification (separate page) 2.2 Printing Multiple Images on One Page The phrase what you see is what you get can be elusive when dealing with images. It is very tricky to print images so that the hard copy matches exactly what is on the screen, because there is usually some interpolation being done by the printer or by the program handling the images. One way to think about this in signal processing terms is that the screen is one kind of D-to-A and the printer is another, but they use different (D-to-A) reconstruction methods to get the continuous-domain (analog) output image that you see. Furthermore, if you try to put two images of different sizes into subplots of the same MATLAB figure, it won t work because MATLAB wants to force them to be the same size. Therefore, you should display your images in separate MATLAB Figure windows. In order to get a printout with multiple images on the same page, use the following procedure: 1. In MATLAB, use show img and trusize to put your images into separate figure windows at the correct pixel resolution. 2. Use an image editing program such as IRFANVIEW to assemble the different images onto one page For each MATLAB figure window, press ALT and the PRINT-SCREEN key at the same time, which will copy the active window contents to the clipboard. 4. After each window capture in step 3, paste the clipboard contents into IRFANVIEW. 5. Arrange the images so that you can make a comparison for your lab report. 6. Print the assembled images to a PDF file or a printer. 6 An alternative is to use the free program called IRFANVIEW, which can do image editing and also has screen capture capability. It can be obtained from 4 McClellan, Schafer and Yoder, Signal Processing First.

5 2.3 Sampling of Images Images that are stored in digital form on a computer have to be sampled images because they are stored in an M N array (i.e., a matrix). The sampling rate in the two spatial dimensions was chosen at the time the image was digitized (in units of samples per inch if the original was a photograph). For example, the image might have been sampled by a scanner where the resolution was chosen to be 300 dpi (dots per inch). 7 If we want a different sampling rate, we can simulate a lower sampling rate by simply throwing away samples in a periodic way. For example, if every other sample is removed, the sampling rate will be halved (in our example, the 300 dpi image would become a 150 dpi image). Usually this is called sub-sampling or down-sampling. 8 Down-sampling throws away samples, so it will shrink the size of the image. This is what is done by the following scheme: when we are downsampling by a factor of p. wp = ww(1:p:end,1:p:end); (a) One potential problem with down-sampling is that aliasing might occur. This can be illustrated in a dramatic fashion with the lighthouse image. Load the lighthouse.mat file which has the image stored in a variable called ww. When you check the size of the image, you ll find that it is not square. Now down-sample the lighthouse image by a factor of 2. What is the size of the down-sampled image? Notice the aliasing in the down-sampled image, which is surprising since no new values are being created by the down-sampling process. Describe how the aliasing appears visually. 9 Which parts of the image show the aliasing effects most dramatically? Instructor Verification (separate page) 3 Lab Exercises: Sampling, Aliasing and Reconstruction 3.1 Down-Sampling For the lighthouse picture, downsampled by two in the warm-up section: (a) Describe how the aliasing appears visually. Compare the original to the downsampled image. Which parts of the image show the aliasing effects most dramatically? (b) This part is challenging: explain why the aliasing happens in the lighthouse image by using a frequency domain explanation. In other words, estimate the frequency of the features that are being aliased. Give this frequency as a number in cycles per pixel. (Note that the fence provides a sort of spatial chirp where the spatial frequency increases from left to right.) Can you relate your frequency estimate to the Sampling Theorem? You might try zooming in on a very small region of both the original and downsampled images. 7 For this example, the sampling periods would be T 1 D T 2 D 1=300 inches. 8 The Sampling Theorem applies to digital images, so there is a Nyquist Rate that depends on the maximum spatial frequency in the image. 9 One difficulty with showing aliasing is that we must display the pixels of the image exactly. This almost never happens because most monitors and printers will perform some sort of interpolation to adjust the size of the image to match the resolution of the device. In MATLAB we can override these size changes by using the function truesize which is part of the Image Processing Toolbox. In the DSP First Toolbox, an equivalent function called trusize.m is provided. 5 McClellan, Schafer and Yoder, Signal Processing First.

6 3.2 Reconstruction of Images When an image has been sampled, we can fill in the missing samples by doing interpolation. For images, this would be analogous to the examples shown in Chapter 4 for sine-wave interpolation which is part of the reconstruction process in a D-to-A converter. We could use a square pulse or a triangular pulse or other pulse shapes for the reconstruction. x[m,n] Repeat Along the Rows Repeat Down the Columns y[m,n] REPEAT SAMPLES EXPANDED Figure 1: 2-D Interpolation broken down into row and column operations: gray dots indicate repeated data values created by a zero-order hold; or, in the case of linear interpolation, they are the interpolated values. For these reconstruction experiments, use the lighthouse image, down-sampled by a factor of 3 (similar to what you did in Section 2.3). Perfrom this down-sampling after loading in the image from lighthouse.mat and saving it in the array called xx. A down-sampled lighthouse image should be created and stored in the variable xx3. The objective will be to reconstruct an approximation to the original lighthouse image, which is , from the smaller down-sampled image. (a) The simplest interpolation would be reconstruction with a square pulse which produces a zero-order hold. Here is a method that works for a one-dimensional signal (i.e., one row or one column of the image), assuming that we start with a row vector xr1, and the result is the row vector xr1hold. xr1 = (-2).ˆ(0:6); L = length(xr1); nn = ceil((0.999:1:4*l)/4); xr1hold = xr1(nn); %<-- Round up to the integer part Plot the vector xr1hold to verify that it is a zero-order hold version derived from xr1. Explain what values are contained in the indexing vector nn. If xr1hold is treated as an interpolated version of xr1, then what is the interpolation factor? Your lab report should include an explanation for this part, but plots are optional use them if they simplify the explanation. (b) Now return to the down-sampled lighthouse image, and process all the rows of xx3 to fill in the missing points. Use the zero-order hold idea from part (a), but do it for an interpolation factor of 3. Call the result xholdrows. Display xholdrows as an image, and compare it to the downsampled image xx3; compare the size of the images as well as their content. (c) Now process all the columns of xholdrows to fill in the missing points in each column and and call the result xhold. Compare the result (xhold) to the original image lighthouse. Include your code for parts (b) and (c) in the lab report. (d) Linear interpolation can be done in MATLAB using the interp1 function (that s interp-one ). When unsure about a command, use help. 6 McClellan, Schafer and Yoder, Signal Processing First.

7 Its default mode is linear interpolation, which is equivalent to using the *linear option, but interp1 can also do other types of polynomial interpolation. Here is an example on a 1-D signal: n1 = 0:6; xr1 = (-2).ˆn1; tti = 0:0.1:6; %-- locations between the n1 indices xr1linear = interp1(n1,xr1,tti); %-- function is INTERP-ONE stem(tti,xr1linear) For the example above, what is the interpolation factor when converting xr1 to xr1linear? (e) In the case of the lighthouse image, you need to carry out a linear interpolation operation on both the rows and columns of the down-sampled image xx3. This requires two calls to the interp1 function, because one call will only process all the columns of a matrix. 10 Name the interpolated output image xxlinear. Include your code for this part in the lab report. (f) Compare xxlinear to the original image lighthouse. Comment on the visual appearance of the reconstructed image versus the original; point out differences and similarities. Can the reconstruction (i.e., zooming) process remove the aliasing effects from the down-sampled lighthouse image? (g) Compare the quality of the linear interpolation result to the zero-order hold result. Point out regions where they differ and try to justify this difference by estimating the local frequency content. In other words, look for regions of low-frequency content and high-frequency content and see how the interpolation quality is dependent on this factor. A couple of questions to think about: Are edges low frequency or high frequency features? Are the fence posts low frequency or high frequency features? Is the background a low frequency or high frequency feature? Comment: You might use MATLAB s zooming feature to show details in small patches of the output image. However, be careful because zooming does its own interpolation, probably a zero-order hold. 10 Use a matrix transpose in between the interpolation calls. The transpose will turn rows into columns. 7 McClellan, Schafer and Yoder, Signal Processing First.

9 Lab: Digital Images: A/D and D/A INSTRUCTOR VERIFICATION SHEET Turn this page in to your lab grading TA before the end of your scheduled Lab time. Name: Date of Lab: Part 2.1(a) Generate and display a digital image. Explain the width of bands in the test image formed from the outer product of ones and a cosine. Give the width of the bands in number of pixels and explain how you can predict that width from the formula for xpix? Verified: Date/Time: Part 2.1(c) Create a image with 5 horizontal black bands separated by white bands. Write the MATLAB code to make this image and display it. Verified: Date/Time: Part 2.3(a) Downsample the lighthouse image to see aliasing. Describe the aliasing, and where it occurs in the image. Verified: Date/Time: 9 McClellan, Schafer and Yoder, Signal Processing First.

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