DIP Lab Manual Using MATLAB

The Digital Image Processing (DIP) Lab Manual Using MATLAB provides comprehensive guidance for performing various image processing experiments using MATLAB software. This manual covers fundamental concepts, practical implementations, and step-by-step procedures for digital image manipulation, enhancement, restoration, segmentation, and analysis. Below are key sections covering theory, experiments, MATLAB commands, and practical applications.

Introduction to Digital Image Processing

Digital Image Processing involves manipulating digital images through computer algorithms. Key components: Image acquisition, preprocessing, enhancement, analysis, and visualization.

MATLAB Environment Setup

Install and configure MATLAB for image processing experiments.

1. Install MATLAB: Ensure Image Processing Toolbox is included in installation.
2. Verify installation: Type 'ver' in command window to check toolbox availability.
3. Set working directory: Use cd command to navigate to image folder.
4. Test basic functions: Use imread, imshow to verify setup.

NOTE! Ensure MATLAB license is active and toolboxes are properly installed.

Basic Image Operations

Fundamental operations for image manipulation in MATLAB.

1. Read image: img = imread('filename.format');
2. Display image: imshow(img); or image(img);
3. Get image information: imfinfo('filename.format');
4. Convert image types: rgb2gray, im2bw, im2double
5. Save image: imwrite(img, 'newfilename.format');

Example Code: img = imread('cameraman.tif'); imshow(img); gray_img = rgb2gray(img);

Image Enhancement Techniques

• Histogram Equalization: histeq() for contrast enhancement
• Brightness Adjustment: imadjust() for gamma correction
• Spatial Filtering: imfilter() for smoothing and sharpening
• Frequency Domain: fft2() for Fourier transform operations
• Noise Removal: medfilt2() for median filtering
• Edge Enhancement: fspecial() with 'sobel', 'prewitt' filters
• Contrast Stretching: imadjust() with specified limits
• Adaptive Histogram: adapthisteq() for local contrast

Image Restoration

Techniques to restore degraded images using MATLAB functions.

Common Methods: Wiener filtering, Lucy-Richardson algorithm, blind deconvolution.

Degradation Modeling: Create point spread function (PSF) using fspecial(). Noise Models: Add Gaussian, salt & pepper noise using imnoise(). Restoration Functions: deconvwnr() for Wiener, deconvlucy() for Richardson-Lucy, deconvblind() for blind deconvolution.

Color Image Processing

Processing color images in different color spaces.

1. RGB Color Space: Direct manipulation of red, green, blue components
2. HSV Conversion: rgb2hsv() for hue-saturation-value space
3. YCbCr Conversion: rgb2ycbcr() for luminance-chrominance
4. Color Segmentation: Based on color thresholds in different spaces
5. Color Balancing: White balance adjustment techniques

Tip: Use color thresholder app for interactive color segmentation.

Image Segmentation

Partitioning images into meaningful regions using various algorithms.

1. Thresholding: graythresh() for Otsu's method
2. Edge Detection: edge() with 'sobel', 'canny', 'prewitt' methods
3. Region Growing: regiongrowing() custom implementation
4. Watershed: watershed() for morphological segmentation
5. Clustering: kmeans() for color-based segmentation
6. Active Contours: activecontour() for boundary detection
7. Texture Segmentation: Using statistical texture features
8. Morphological Operations: For post-processing segmented regions

WARNING! Choose appropriate method based on image characteristics and application requirements.

Morphological Operations

Shape-based operations for binary and grayscale images. Structuring Element: strel() to create morphological kernels. Basic Operations: imdilate(), imerode(), imopen(), imclose(). Advanced Operations: imtophat(), imbothat() for background correction.

Image Compression

Reducing image file size while maintaining quality.

Lossless Compression: imwrite() with PNG format. Lossy Compression: imwrite() with JPEG quality parameter. Transform Coding: dct2() for discrete cosine transform. Wavelet Compression: wavedec2() for multi-resolution analysis.

CAUTION! Balance between compression ratio and image quality based on application needs.

Practical Applications

Real-world applications of digital image processing using MATLAB.

Implementation: Combine multiple techniques for complex applications.

Troubleshooting & FAQs

Resources: MATLAB documentation, Image Processing Toolbox user guide

Support: MathWorks technical support, online MATLAB communities