Image Enhancement is a fundamental concept in Digital Image Processing (DIP) and Computer Vision,
Image Enhancement is a fundamental concept in Digital Image Processing (DIP) and Computer Vision, widely used in fields like medical imaging, satellite imagery, machine learning, AI, and pattern recognition. In this blog, we will learn how to perform image enhancement using MATLAB and Python, with simple explanations, practical methods, and exam-oriented understanding. This guide is perfect for B.Tech CSE students, BEU exam preparation, lab work, and mini projects.
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What is Image Enhancement?
Image enhancement is the process of improving the visual quality of an image so that it becomes more suitable for human interpretation or machine analysis. Objectives of Image Enhancement: Improve image contrast Reduce noise Highlight important features Increase clarity for analysis
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Types of Image Enhancement Techniques
1. Spatial Domain Enhancement
Operations are performed directly on image pixels. Examples: Contrast Stretching Image Negative Log Transformation Histogram Equalization
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2. Frequency Domain Enhancement
Operations are performed on the frequency representation of the image. Examples: Low Pass Filtering High Pass Filtering Fourier Transform based enhancement
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Image Enhancement Using MATLAB
MATLAB is widely used in engineering labs and exams for image processing.
Step 1: Read an Image
img = imread('image.jpg'); imshow(img); title('Original Image');
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Step 2: Convert to Grayscale
gray = rgb2gray(img); imshow(gray); title('Grayscale Image');
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Step 3: Contrast Enhancement
enhanced = imadjust(gray); imshow(enhanced); title('Contrast Enhanced Image');
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Step 4: Histogram Equalization
histeq_img = histeq(gray); imshow(histeq_img); title('Histogram Equalized Image'); Used in: Medical images, satellite images, low-contrast photos
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Image Enhancement Using Python
Python is popular due to OpenCV, NumPy, and Matplotlib, and widely used in AI & ML projects.
Required Libraries
import cv2 import numpy as np import matplotlib.pyplot as plt img = cv2.imread('image.jpg') img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) plt.imshow(img) plt.title("Original Image") plt.axis('off') gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) plt.imshow(gray, cmap='gray') plt.title("Grayscale Image") plt.axis('off') enhanced = cv2.equalizeHist(gray) plt.imshow(enhanced, cmap='gray') plt.title("Enhanced Image") plt.axis('off') blur = cv2.GaussianBlur(enhanced, (5,5), 0) plt.imshow(blur, cmap='gray') plt.title("Noise Reduced Image") plt.axis('off')
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Applications of Image Enhancement
Medical Imaging (X-ray, MRI) Satellite & Remote Sensing Machine Learning & AI Face Recognition Systems Object Detection Robotics & Automation
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Exam & Interview Perspective
📌 Important for Subjects: Digital Image Processing Computer Vision Artificial Intelligence Machine Learning 📌 Common Exam Questions: Define image enhancement Explain histogram equalization Write MATLAB/Python code for image enhancement