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A Parametric Logarithmic Image Processing Framework Based on Fuzzy Graylevel Accumulation by the Hamacher T-Conorm.

Constantin Vertan¹, Corneliu Florea¹, Laura Florea¹

¹LAPI-The Image Analysis and Processing Laboratory, Politehnica University of Bucharest, 060042 Bucharest, Romania.

Sensors (Basel, Switzerland)

|July 24, 2021

Summary

Logarithmic Image Processing (LIP) models enhance digital images by enabling graylevel operations without clipping. A generalized LIP framework unifies existing models using fuzzy logic and Hamacher conorms for image processing.

Keywords:

T-conorms fuzzy image processing image processing models logarithmic image processing

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Area of Science:

Digital Image Processing
Computer Vision
Fuzzy Logic

Background:

Logarithmic Image Processing (LIP) models are effective for digital image visualization and enhancement.
Standard LIP models facilitate graylevel arithmetic (addition, subtraction, multiplication) within a fixed range, avoiding clipping.

Purpose of the Study:

To propose a generalized Logarithmic Image Processing (LIP) framework.
To unify existing LIP and LIP-like models under a single parameterized family.

Main Methods:

Fuzzy modeling of gray level addition as an accumulation process.
Utilizing the Hamacher conorm to describe the accumulation process.
Developing a parameterized family of LIP models.

Main Results:

A generalized LIP framework encompassing existing models was constructed.
The framework is based on fuzzy modeling of gray level addition via Hamacher conorms.
All current LIP and similar models are shown to be special cases within this framework.

Conclusions:

The proposed generalized LIP framework offers a unified approach to various LIP models.
This framework effectively models gray level addition as a fuzzy accumulation process.
The generalization is applicable to real-world digital image processing scenarios.