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Texture enhanced histogram equalization using TV- L¹ image decomposition.

Ovidiu Ghita¹, Dana E Ilea, Paul F Whelan

¹Centre for Image Processing and Analysis, School of Electronic Engineering, Dublin City University, Dublin 9, Ireland. ghitao@eeng.dcu.ie

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 8, 2013

Summary

This summary is machine-generated.

This study introduces a new variational approach for image enhancement, improving contrast enhancement by using texture information to reduce intensity saturation and maximize image information content.

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Area of Science:

Image Processing
Computer Vision
Applied Mathematics

Background:

Histogram equalization is a common image processing technique for contrast enhancement.
Standard methods can cause intensity saturation, degrading image quality.
Existing approaches often overlook texture information.

Purpose of the Study:

To develop a novel variational approach for image enhancement.
To alleviate intensity saturation effects in contrast enhancement.
To improve contrast enhancement by incorporating texture information.

Main Methods:

Decomposition of images into cartoon and texture components using total variation (TV) minimization with an L(1) fidelity term.
Utilizing texture information to emphasize local textural features in contrast enhancement.
Implementation of a nonlinear histogram warping strategy for contrast enhancement.

Main Results:

The proposed method effectively reduces intensity saturation compared to standard techniques.
Incorporating texture information enhances the contribution of local features.
Experimental results demonstrate superior performance over conventional contrast enhancement strategies.
The nonlinear histogram warping maximizes information content in the enhanced images.

Conclusions:

The new variational approach offers improved image enhancement by addressing intensity saturation.
Leveraging texture information provides a more effective contrast enhancement strategy.
The method shows significant potential for various image processing applications.