unit 3 IMAGE ENHANCEMENT

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Introduction to Image Enhancement

Image Enhancement refers to improving the visual appearance of an image or to convert the image to a form better suited for analysis by a human or machine.

  • Goal: Highlight important features and suppress irrelevant details.

  • Applied in areas like medical imaging, satellite imaging, robot vision, etc.

2. Point Operations

Operations that modify each pixel independently without considering neighboring pixels.

Types:

  • Image Negative: Inverts the intensities to highlight hidden details.

  • Log Transformation: Expands dark pixel values and compresses bright values.

  • Power-Law (Gamma) Transformation: Controls overall brightness.

  • Contrast Stretching: Increases dynamic range of pixel intensity.

  • Thresholding: Converts image into binary by setting a threshold.

Formula Example for Negative:

s=L−1−rs = L - 1 - rWhere:

  • rr = input pixel

  • ss = output pixel

  • LL = maximum intensity level (256 for 8-bit)

3. Histogram Modeling

A histogram represents the frequency distribution of intensity levels in an image.

Key Techniques:

  • Histogram Equalization:

    • Improves contrast by redistributing pixel intensities.

    • Makes the histogram uniform.

  • Histogram Specification (Matching):

    • Adjusts the histogram to match a specified distribution.

Use:

  • Brightening dark images

  • Enhancing contrast without prior information.

4. Filtering and Spatial Operations

Operations that modify a pixel based on its neighboring pixels.

Spatial Filters Types:

TypePurpose
Low-Pass Filter (Smoothing)Reduces noise, blurs edges.
High-Pass Filter (Sharpening)Enhances edges and fine details.
Median FilterRemoves "salt-and-pepper" noise.
Mean FilterSmooths by averaging neighborhood pixels.

Spatial Operations:

  • Convolution: Applying a mask/kernel across the image.

  • Gradient Operators (Sobel, Prewitt): Detect edges.

5. Transform Operations

Enhancement using transformations in a different domain (frequency domain).

Key Techniques:

  • Fourier Transform: Enhances based on frequency content.

  • Hadamard Transform: Enhances using square wave decomposition.

  • Discrete Cosine Transform (DCT): Highlights important low-frequency components.

  • Wavelet Transform: Provides multi-resolution analysis for local and global features.

Use:

  • Enhancing textures, denoising, image restoration.

6. Multi-Spectral Image Enhancement

Enhancing images that have multiple bands (e.g., satellite images with visible, infrared, etc.).

Key Techniques:

  • Band Combination: Merging different spectral bands to create a new image.

  • Principal Component Analysis (PCA): Reduces redundancy and enhances major variations.

  • Color Composite Techniques: Assigns different spectral bands to RGB channels to highlight features.

Applications:

  • Environmental monitoring

  • Agriculture (crop health)

  • Urban planning (land cover classification)

Summary Chart

CategoryTechniques
Point OperationsNegative, log, gamma, contrast stretch
Histogram ModelingEqualization, specification
Spatial FilteringMean, Median, Sobel, Prewitt
Transform DomainFourier, Hadamard, DCT, Wavelet
Multi-SpectralPCA, band combination, color composites

In Short:

  • Image enhancement is about making an image more useful or attractive.

  • It can be done in spatial domain (point, neighborhood operations) or transform domain (frequency based).

  • Multi-spectral images allow new dimensions of enhancement beyond human vision.

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