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Histogram

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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Related Experiment Video

Updated: May 16, 2026

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Defocus blur parameters identification by histogram matching.

Huei-Yung Lin¹, Xin-Han Chou

¹Department of Electrical Engineering and Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, 168 University Rd., Min-Hsiung, Chiayi 621, Taiwan. hylin@ccu.edu.tw

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|December 4, 2012

Summary

This summary is machine-generated.

This study introduces a novel technique for identifying camera defocus blur using histogram analysis of real edge images. The method accurately determines blur extent by matching synthesized and real defocused image regions.

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Last Updated: May 16, 2026

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

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Published on: February 8, 2014

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

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Published on: April 4, 2017

Area of Science:

Image processing
Computer vision
Computational photography

Background:

Camera defocus blur affects image quality and requires accurate identification for applications.
Existing methods may not fully account for complex camera response and noise characteristics.

Purpose of the Study:

To develop and validate a robust technique for identifying defocus blur in real edge images.
To formulate the image defocus process considering nonlinear camera response and intensity-dependent noise.

Main Methods:

Utilizing histogram analysis of real edge images for blur identification.
Formulating the image defocus process with nonlinear camera response and intensity-dependent noise models.
Employing histogram matching with intensity-dependent filtering and iterative point-spread function parameter adjustment.

Main Results:

Demonstrated robustness and feasibility of the proposed defocus blur identification technique.
Successful application on both synthetic and real edge images.
Accurate identification of blur extent through histogram comparison.

Conclusions:

The proposed histogram-based technique provides an effective solution for defocus blur identification.
The method's ability to model camera nonlinearities and noise enhances its practical applicability.
This approach offers a reliable tool for image quality assessment and restoration.