Abstract
Images captured by cameras are sometimes contaminated either during acquisition or transmission. Therefore, a preprocessing step is required which reduces noise from images. In this paper, a novel and efficient edge preserving universal noise removal algorithm is proposed which exploits both the local and global characteristics of the neighboring non-corrupted pixels. In the proposed algorithm, corrupted pixels are detected by robust outlying ratio (ROR) and replaced with the weighted sum (local characteristics) of the neighboring non-corrupted pixels in 3 × 3 window and these weights are obtained by solving the kernel ridge regression (KRR) which uses the global mean and covariance (global characteristics). Extensive experimental results demonstrate that our algorithm has better noise removal capability in terms of both objective and subjective evaluation as compared to existing denoising algorithms.
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Appendix
Appendix
ROR | robust outlying ratio |
KRR | kernel ridge regression |
RVIN | Random valued impulse noise |
SPN | salt and pepper noise |
SMF | Standard median filter |
ACWF | adaptive center weighted median filter |
PWMAD | pixel-wise median absolute deviation |
CAFSM | cluster based adaptive fuzzy switching median |
ROLD-EPR | rank-ordered logarithmic difference edge preserving |
GABF | Gaussian-adaptive bilateral filter |
CNN | Convolutional Neural Network |
SVD | singular value decomposition |
ROAD | rank-ordered absolute difference |
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Khan, S., Singh, Y.V. & Rai, A.K. An efficient edge preserving universal noise removal algorithm using kernel ridge regression. Multimed Tools Appl 81, 19863–19877 (2022). https://doi.org/10.1007/s11042-021-11274-4
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DOI: https://doi.org/10.1007/s11042-021-11274-4