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Related Concept Videos

Histogram01:05

Histogram

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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Probability Histograms01:17

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Color Vision01:24

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Indicators

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Certain organic substances change color in dilute solution when the hydronium ion concentration reaches a particular value. For example, phenolphthalein is a colorless substance in any aqueous solution with a hydronium ion concentration greater than 5.0 × 10−9 M (pH < 8.3). In more basic solutions where the hydronium ion concentration is less than 5.0 × 10−9 M (pH > 8.3), it is red or pink. Substances such as phenolphthalein, which can be used to determine the pH of a solution, are...
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Relative Frequency Histogram01:14

Relative Frequency Histogram

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Updated: Mar 18, 2026

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Hue-preserving and saturation-improved color histogram equalization algorithm.

Ki Sun Song, Hee Kang, Moon Gi Kang

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |July 14, 2016
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    Summary
    This summary is machine-generated.

    This study introduces a novel contrast enhancement (CE) algorithm that improves image contrast and saturation without degrading color. The algorithm overcomes limitations of local histogram equalization (HE) by reducing artifacts and preserving hue, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Local histogram equalization (HE) improves contrast but can cause artifacts due to block-based processing.
    • Global HE is less prone to artifacts but offers suboptimal contrast enhancement compared to local HE.

    Purpose of the Study:

    • To develop a contrast enhancement (CE) algorithm that combines the benefits of global and local HE methods.
    • To address color degradation and saturation issues in existing CE algorithms for color images.

    Main Methods:

    • Proposed a CE algorithm integrating global HE characteristics into local HE to mitigate artifacts.
    • Employed channel adaptive equalization and similarity of ratios between color channels to manage saturation and color fidelity.
    • Evaluated two application approaches: luminance processing and per-channel processing.

    Main Results:

    • The proposed CE algorithm effectively enhances both global and local contrasts.
    • Achieved improved image saturation and contrast without introducing color degradation or hue shifts.
    • Demonstrated superior performance over existing methods based on objective evaluation metrics.

    Conclusions:

    • The novel CE algorithm successfully enhances image contrast and saturation while preserving original color characteristics.
    • The method effectively overcomes the artifact and color degradation issues associated with traditional HE techniques.
    • Offers a robust solution for high-quality color image enhancement.