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Related Concept Videos

Histogram01:05

Histogram

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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Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
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Automatic exact histogram specification for contrast enhancement and visual system based quantitative evaluation.

Debashis Sen1, Sankar K Pal

  • 1Center for Soft Computing Research, Indian Statistical Institute, Kolkata, 700108 India. dsen_t@isical.ac.in

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 7, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic histogram specification technique for superior image contrast enhancement. The novel method improves image information and reduces ambiguity for better visual quality.

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Histogram equalization is a common technique for image contrast enhancement.
  • Existing methods may lack precision or automatic adjustment for optimal results.

Purpose of the Study:

  • To propose an automatic exact histogram specification technique for global and local image contrast enhancement.
  • To develop a new, multi-scale image contrast measure based on a local band-limited approach and retinal receptive field models.

Main Methods:

  • An automatic exact histogram specification technique involving histogram modification and information/ambiguity maximization.
  • A novel contrast measurement method using a local band-limited approach, center-surround retinal receptive field model, and L(p)-norm across multiple scales.

Main Results:

  • The proposed automatic histogram specification effectively enhances image contrasts.
  • Qualitative and quantitative analyses demonstrate the superiority of the new technique over existing methods.

Conclusions:

  • The developed automatic exact histogram specification technique offers significant improvements in image contrast enhancement.
  • The new image contrast measure provides a robust quantitative evaluation of enhancement effectiveness.