📊 Histogram Equalization Visualizer

Upload an image and explore grayscale conversion, histogram analysis, histogram equalization and its mathematics.

📁 Upload Image

Please upload an image to begin.

1. Original Image

2. Grayscale Image

3. Grayscale Histogram

4. Histogram Values

Gray Level	Frequency	Probability	CDF	New Gray Level

5. Equalized Image

6. Histogram After Equalization

🧮 Histogram Equalization Mathematics

Step 1: Find the frequency

For every grayscale intensity r_k, count how many pixels have that intensity.

n_k = Number of pixels having intensity r_k

Step 2: Calculate probability

Let n be the total number of pixels.

p(r_k) = n_k / n

Step 3: Calculate cumulative distribution function (CDF)

CDF(r_k) = Σ p(r_j)    for j = 0 to k

The CDF adds the probabilities from the first gray level up to the current gray level.

Step 4: Histogram equalization formula

s_k = (L - 1) × CDF(r_k)

Where:

• L = number of possible gray levels
• For an 8-bit image, L = 256
• Therefore L − 1 = 255
• r_k = original gray level
• s_k = new equalized gray level

Step 5: Example calculation

Suppose an intensity level has:

CDF = 0.60

For an 8-bit image:

s = 255 × 0.60

s = 153

Therefore, the original gray level is mapped to approximately gray level 153.

Complete process

Image → Grayscale → Histogram → Probability → CDF → Mapping → Equalized Image

Interactive Image Processing Demonstration — Grayscale & Histogram Equalization