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Related Concept Videos

Histogram01:05

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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).
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Local Contrast-Based Pixel Ordering for Exact Histogram Specification.

Kohei Inoue1, Naoki Ono1, Kenji Hara1

  • 1Department of Media Design, Faculty of Design, Kyushu University, 4-9-1 Shiobaru, Minamiku, Fukuoka 815-8540, Japan.

Journal of Imaging
|September 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel pixel ordering method for precise histogram specification. It enhances image contrast by avoiding artifacts seen in existing techniques.

Keywords:
Gaussian filtercontrast enhancementexact histogram equalizationexact histogram specificationpixel ordering

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Area of Science:

  • Computer Vision
  • Image Processing
  • Digital Signal Processing

Background:

  • Histogram equalization is fundamental for image contrast enhancement.
  • Histogram specification offers generalized contrast control by matching target histograms.
  • Existing methods for exact histogram specification can produce undesirable artifacts.

Purpose of the Study:

  • To propose a robust method for exact histogram specification.
  • To address limitations of current pixel ordering techniques in histogram specification.
  • To extend the histogram specification method to color images.

Main Methods:

  • A novel pixel ordering approach based on local pixel contrast.
  • Utilizing an unapproximated Gaussian filter to prevent pixel value duplication.
  • Decomposing the pixel ordering problem into subproblems for efficient merging.
  • Extending the method from grayscale to color image processing.

Main Results:

  • The proposed method effectively alleviates false patterns generated by state-of-the-art techniques.
  • Experimental results demonstrate superior performance in achieving exact histogram specification.
  • The method ensures accurate histogram matching without introducing artifacts.

Conclusions:

  • The proposed local contrast-based pixel ordering is effective for exact histogram specification.
  • This approach offers a more reliable alternative to existing histogram specification methods.
  • The technique is successfully extended for contrast enhancement in color images.