Region of interest contrast measures

Reměs, Václav; Haindl, Michal

doi:10.14736/kyb-2018-5-0978

Cited by 5 publications

(1 citation statement)

References 25 publications

(39 reference statements)

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“…Finally, we make a comprehensive comparison from six evaluation metrics: information entropy, 23 enhancement measure evaluation (EME), 24 contrast, 25 average gradient, 26 standard deviation, 27 and running time. These six indexes reflect the information richness, detail performance, overall performance, sharpness of the image, image information distribution, and algorithm complexity.…”

Section: Experimental Settingmentioning

confidence: 99%

Underwater polarization image fusion based on NSCT

Liu

Wang

et al. 2023

J. Electron. Imag.

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Affected by organic matter and suspended particles in the underwater environment, underwater images suffer from information loss and low contrast, and the underwater images obtained by traditional visible light imaging techniques are not effective. Based on this problem, a non-subsampled contourlet transform (NSCT)-based underwater polarization image fusion method is proposed in this paper. First, we use a division of time polarimeter and polarization imaging technique to acquire underwater target images. Then, the contrast limited adaptive histogram equalization algorithm and the two-dimensional median filtering algorithm preprocess the visible image and the degree of polarization image, respectively. After that, the lowfrequency and high-frequency sub-bands of the images are decomposed by the non-subsampled contourlet transform. The fusion rule for the low-frequency subband adopts the adaptive fuzzy method. For the high-frequency sub-bands, the fusion rule of taking the larger absolute value in pixels is used. In the experiment, popular methods were used to compare the advantages of the proposed method. The experimental results show that the proposed method is effective in improving the evaluation indexes, such as information entropy, enhancement measure evaluation, contrast, average gradient, and standard deviation. And the visual effect is much more comfortable to observe. The method is an effective fusion algorithm that can be applied to multiple material targets to improve the quality of underwater polarization imaging. The code is available at a Github repository at: https:// github.com/Si-Yu12/Underwater-polarization-image-fusion-based-on-NSCT.

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