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Reducing integrability error of color tensor gradients for image fusion
Roberto Montagna1, Graham D Finlayson
1School of Computing Sciences, University of East Anglia, Norwich, U.K. r.montagna@uea.ac.uk
Summary
Improving Di Zenzo's color tensor gradients for image processing involves desaturating images. This enhances gradient integrability, reducing artifacts and improving contrast, leading to preferred image fusion results.
Area of Science:
- Computer Vision
- Image Processing
- Color Science
Background:
- Gradient-based operators face challenges with color images.
- Di Zenzo's color tensor offers a solution for multichannel image gradients.
- Existing methods for reintegrating gradients can cause artifacts like edge smearing.
Purpose of the Study:
- To improve the integrability of Di Zenzo's color tensor gradients.
- To explore the impact of image saturation on gradient integrability.
- To enhance multispectral image visualization and contrast.
Main Methods:
- Investigated the effect of desaturation (HSV color space) on color tensor gradients.
- Extended saturation concepts to multispectral images.
- Analyzed tensor gradient behavior in logarithmic color space.
- Applied retinex algorithms for contrast enhancement and gradient reintegration.
Main Results:
- Desaturation significantly improves the integrability of Di Zenzo's gradients.
- Changes in saturation in linear space yield improvements in logarithmic color space.
- Log-differences are perceptually more meaningful.
- Retinex algorithms enhance contrast and aid gradient reintegration.
Conclusions:
- Desaturation is a simple yet effective method to improve color tensor gradient integrability.
- Combining desaturation with retinex algorithms offers superior contrast enhancement and artifact reduction.
- The proposed method is psychophysically preferred over other image fusion techniques.
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