Mastering MATLAB: A Comprehensive Guide to Solving Complex Assignment Questions
MATLAB is a powerful tool used by engineers, scientists, and mathematicians for data analysis, visualization, and algorithm development. Its applications span various domains, making it an essential skill for students in these fields. In this blog, we will tackle a challenging university-level MATLAB assignment question on image processing, explaining the concepts involved and providing a step-by-step guide to solving it.
The Assignment Question
Question: Using MATLAB, develop a script to enhance the contrast of a grayscale image using histogram equalization. Discuss the theoretical background of histogram equalization and provide a detailed explanation of how your script works.
Understanding Histogram Equalization
Theoretical Background
Histogram equalization is a technique in image processing that improves the contrast of images. It works by spreading out the most frequent intensity values, enhancing the global contrast of the image. This method is particularly useful for images that are either too dark or too bright.
How Histogram Equalization Works
1. Histogram Calculation: The first step is to compute the histogram of the grayscale image, which shows the distribution of pixel intensities.
2. Cumulative Distribution Function (CDF): Next, we calculate the CDF from the histogram. The CDF helps in mapping the original intensity values to new values that span the entire range.
3. Mapping Intensities: Using the CDF, we map the original intensity values to new values to achieve a more uniform distribution of intensities.
4. Apply the Mapping: Finally, we apply this mapping to the original image to get the enhanced image.
Step-by-Step Guide to Solving the Assignment
Step 1: Load the Image
Begin by loading the grayscale image into MATLAB. You can use the imread function to read the image file.
Step 2: Compute the Histogram
Use the imhist function to calculate the histogram of the image. This function returns the number of pixels for each intensity level.
Step 3: Calculate the CDF
Calculate the cumulative distribution function (CDF) from the histogram. The CDF is obtained by cumulatively summing the histogram values and then normalizing them.
Step 4: Create the Mapping Function
Use the CDF to create a mapping function that will transform the original pixel intensities to the new, equalized values. This involves scaling the CDF values to fit the intensity range of the image (usually 0 to 255).
Step 5: Apply the Mapping
Apply the mapping to the original image using array indexing. This step transforms the original image to the contrast-enhanced image.
Step 6: Display the Results
Finally, use MATLAB's imshow function to display the original and the enhanced images side by side for comparison. You can also display the histograms of both images using the subplot and imhist functions to visualize the improvement in contrast.
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