Nonlinear Contrast Enhancement

🔹 What is Contrast Enhancement?

Contrast enhancement improves the difference between light and dark areas of an image so that important features become more visible.
It changes the Digital Number (DN) values (brightness values) of pixels to make the image clearer and easier to interpret.

There are two main types of contrast enhancement:

Linear (simple stretching or scaling of pixel values)
Nonlinear (based on the statistical distribution of pixel values)

Here, we focus on Nonlinear Contrast Enhancement methods.

⚙️ 1. Histogram Equalization

🔸 Concept:

In this method, all pixel brightness values (DNs) are redistributed so that there are roughly an equal number of pixels for each possible brightness level.
The result is a flatter histogram, meaning the image uses the full range of brightness values more evenly.

🔸 Purpose:

To increase contrast in areas where pixel values are heavily concentrated (for example, dark or light regions).

🔸 Example:

If an image is mostly dark, histogram equalization spreads out the pixel values across the entire brightness range — making hidden details visible.

⚙️ 2. Histogram Normalization (Gaussian Stretch)

🔸 Concept:

Here, the image's histogram (distribution of brightness values) is adjusted to fit a normal or Gaussian curve.
A Gaussian curve looks like a smooth bell-shaped curve centered around the mean brightness.

🔸 Purpose:

To make the image's brightness distribution look more "natural," similar to how light and shadow appear in real life.

🔸 Example:

If an image has too many very bright or very dark pixels, Gaussian stretching balances it by redistributing pixel values around the mean brightness level.

⚙️ 3. Reference Stretch (Histogram Matching)

🔸 Concept:

This method matches the histogram of one image (the "input image") to that of another image (the "reference image").
It uses a lookup table (LUT) — a list of values that tells how each pixel in the input image should be changed to match the reference image.

🔸 Purpose:

To make two images look similar in brightness and contrast — useful when comparing satellite images from different dates or sensors.

🔸 Example:

If you want a 2024 satellite image to look like a 2021 one (for comparison), histogram matching adjusts the tone and brightness so both look similar.

⚙️ 4. Density Slicing (Level Slicing)

🔸 Concept:

This method groups ranges of DN values into a few distinct levels or colors.
Several brightness values are "lumped together" and displayed as a single value or color.

🔸 Purpose:

To highlight specific features or ranges of interest (like vegetation, water, or built-up areas) based on brightness.

🔸 Example:

Pixels with DNs between 30–80 might all be shown in green (vegetation), and those between 120–180 in blue (water).

🔸 Note:

This is often used to create pseudo-color images — where grayscale images are converted into color for easier interpretation.

⚙️ 5. Thresholding

🔸 Concept:

Thresholding divides the image into two groups based on a chosen DN (threshold value):
- Pixels below the threshold → black (0)
- Pixels above the threshold → white (1)

🔸 Purpose:

To separate or extract specific features — for example, distinguishing water from land, or urban areas from vegetation.

🔸 Example:

If DN < 100 → water (black)
If DN ≥ 100 → land (white)

🔸 Type:

This produces a binary image — made of only two values (0 and 1).

Method	Main Idea	Output Effect	Common Use
Histogram Equalization	Redistribute pixel values to flatten histogram	Enhances contrast across full range	General image enhancement
Histogram Normalization (Gaussian Stretch)	Fit histogram to normal (bell-shaped) curve	Natural-looking brightness	Correct uneven illumination
Histogram Matching	Match histogram to another image	Uniform tone between images	Multi-date or multi-sensor comparison
Density Slicing	Group DN ranges into specific colors	Highlight features	Land cover or vegetation mapping
Thresholding	Split image into two brightness levels	Binary (black-white) image	Feature extraction (e.g., water bodies)

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