Lab Report #8 Image Processing

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Rochester Institute of Technology *

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Electrical Engineering

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Apr 3, 2024

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Lab #8 Image Processing Laboratory Report Fundaments of Imaging Science Instructor: Dr. Anthony Vodacek Stephen D. Gayle Section 1 1 8/26/2019

Introduction The purpose of this experiment is to apply some digital image processing techniques to a series of grayscale and color images to learn how these techniques work to enhance image content. Digital image processing is widely used to enhance or extract features from images and to manipulate the displays of images. The goal is often to make the visual display of images better by increasing contrast or highlighting certain parts of an image. In some cases, the processing can lead to information about the image content that is not necessarily displayed but may be a numerical quantity or an identification of image content. This lab will give you experience with manipulating histograms, applying median filters, mean filters, edge detection filters, manipulating color images, and using Lookup Tables to enhance grayscale images. Methods For this lab, I used the ImageJ program to process the images. I downloaded the figure 1 from the ImageJ site and then started processing it. Following the steps within the Lab guide produced figures 1A, 1B, 1BA, 1C and 1D. Figure 1A came from analyzing figure 1 and put into the form of a histogram. Figure 1B came from adjusting the brightness of the image to display no black on the image, which is called stretch. Figure 1BA came from analyzing figure 1B and put into the form of a histogram. Figure 1C came from the noise reduction of figure 1B using the median filter. Figure 1D came from the same process as figure 1C but used the mean filer instead. Next, I downloaded the figure 2 from the ImageJ site and then started processing it. Following the steps within the Lab guide produced figures 2A, 2B and 2C. Figure 2A came from changing the kernel to numbers that will detect horizontal lines. Figure 2B came from changing the kernel to numbers that will detect vertical lines. Figure 2C came from the combining of the figures 2A and 2B. Then, I downloaded the figure 3 from the ImageJ site and then started processing it. Following the steps within the Lab guide produced figures 3A, 3B, 3C and 3D. Figure 3A came from splitting the colors of the image into red, green and blue and stretching(just like figure 1B) them to a point where each of them was showing the least amount of black, but not dipping into overexposure and then merging the 3 images back to one. Figure 3B came from once again splitting the colors of the original image but applying a mean filter over the blue split image with a large radius, which leads to blurring of the blue split image and then merging the 3 images into one. Figure 3C is the same process as Figure 3B but blurring green instead. Figure 3D is the same process as Figure 3B but blurring red instead. Lastly, I downloaded the figure 4 from the ImageJ site and then started processing it. Following the steps within the Lab guide produced figures 4A, 4B and 4C. For all three I played around with all the LUT’s until I found what looked appealing (figure 4A) and 2 that looked gross to me (figure 4B and 4C). 2 8/26/2019

Figure 1 – Enhance me Figure 2 – Boat 3 8/26/2019

Figure 3 – Baboon Figure 4 – TDM filter Results Figure 1A – Histogram for figure 1 4 8/26/2019

Figure 1B – Enhance me stretched Figure 1B looks much clearer image-wise, unlike the original (figure 1) where you can barely see the face. All there is left to do is get rid of the noise. Figure 1BA – Histogram of Enhance me stretched Figure 1BA is the histogram of figure 1B 5 8/26/2019

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