Reading this result: Equalization remaps intensities so the cumulative histogram becomes roughly linear, spreading the 40-level input across nearly the full 0–255 range (std 7.7 → 75.4). Contrast jumps because rarely-used levels are compressed and crowded levels are pulled apart — which also amplifies any noise in flat regions.
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How it works
Histogram equalization builds the intensity histogram of an image, forms its cumulative distribution function (CDF), and uses that CDF as a transfer function s = (L-1)·CDF(r) to spread crowded intensity levels across the full 0–255 range. Compare it against a linear min–max contrast stretch and a gamma tone curve on the same low-contrast, dark, washed-out, or bimodal test image — and see why equalization boosts local detail but can amplify noise in flat regions.
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Is this histogram equalization contrast tool really free?▾
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The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.