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Human visual system-based image enhancement and logarithmic contrast measure.

Karen A Panetta¹, Eric J Wharton, Sos S Agaian

¹Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA. karen@eecs.tufts.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 14, 2008

Summary

This summary is machine-generated.

This study introduces novel image enhancement algorithms for machine vision, addressing varying illumination challenges. New quantitative measures enable automated parameter selection for improved contrast and detail preservation.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Varying scene illumination presents significant challenges for machine vision systems.
Developing effective global image enhancement methods for diverse lighting conditions is crucial.
Existing methods often struggle with preserving details and correcting non-uniform illumination.

Purpose of the Study:

To introduce novel image enhancement algorithms for improved performance under varying illumination.
To develop quantitative measures for automated selection of image enhancement parameters.
To enhance the robustness and efficiency of machine vision systems in challenging lighting.

Main Methods:

Developed an edge-preserving contrast enhancement algorithm for detail preservation.
Introduced a human visual system (HVS)-based multihistogram equalization for non-uniform illumination correction.
Proposed new quantitative measures: logarithmic Michelson contrast measure (AME) and logarithmic AME by entropy for automated parameter tuning.

Main Results:

The proposed algorithms demonstrate effective contrast enhancement while preserving edge details.
HVS-based multihistogram equalization efficiently corrects non-uniform illumination.
New quantitative measures allow for objective and automated parameter selection, outperforming subjective methods.

Conclusions:

The novel image enhancement techniques offer significant improvements for machine vision under challenging illumination.
Automated parameter selection using new quantitative measures enhances the practicality and performance of image enhancement.
These advancements contribute to more robust and reliable machine vision systems.