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Histogram Equalization Using A Selective Filter.

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Histogram Equalization Using A Selective Filter.

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Histogram equalization using a selective filter.

Roberto M Dyke¹, Kai Hormann¹

¹Faculty of Informatics, Università della Svizzera italiana, Via Buffi 13, 6900 Lugano, Switzerland.

The Visual Computer

|November 16, 2023

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel histogram equalization technique that improves upon existing methods by ensuring more uniform histograms. The new approach enhances image contrast while preserving intensity detail, benefiting image processing applications.

Keywords:

Dequantization Histogram equalization Histogram matching Image enhancement

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Area of Science:

Computer Vision
Image Processing
Digital Signal Processing

Background:

Popular image processing software often uses a naive histogram equalization method, resulting in non-uniform histograms.
Existing exact histogram equalization techniques can introduce undesirable artifacts.
There is a need for improved histogram equalization methods that balance uniformity with artifact avoidance.

Purpose of the Study:

To bridge the gap between continuous theory and discrete implementation of global histogram equalization.
To develop a novel histogram equalization technique that improves upon the naive approach.
To achieve a more uniform histogram with preserved intensity distances and high entropy.

Main Methods:

Formulated a novel histogram equalization technique based on continuous theory.

Employed linear interpolation of the cumulative distribution for low-bit images.

Used selective box filtering for approximate dequantization of intensities.

Main Results:

The proposed method produces an equalized histogram with high entropy.
Distances between similar intensity values are preserved.
The technique offers improvements over existing naive and exact histogram equalization methods.

Conclusions:

The novel histogram equalization technique effectively enhances image contrast and uniformity.
The method avoids artifacts associated with some exact techniques.
This approach has potential applications in related image processing tasks like edge detection.