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A Comparative Analysis of the Algorithms for De-noising Images Contaminated with Impulse Noise

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Abstract

Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively.

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Abbreviations

ACWMF:

Adaptive center weighted median filter

ADWMF:

Adaptive dynamically weighted median filter

AMF:

Adaptive median filter

AMFLPD:

Adaptive median filtering based on local pixel distribution

APF:

Adaptive probability filter

ASMF:

Adaptive switching median filter

ASWMF:

Adaptive switching weighted median filter

ATF:

Adaptive trimmed filter

ATMF:

Alpha trimmed median filter

BDND:

Switching median filter with boundary discriminative noise detection

BN:

Batch normalization

BPDF:

Based on pixel density function

CT:

Computation time

FEMF:

Fast and efficient median filter

IEF:

Image enhancement factor

MAE:

Mean absolute error

MDBUTMF:

Modified decision based un-symmetric trimmed median filter

MRI:

Magnetic resonance imaging

MSE:

Mean square error

NAFSM:

Noise adaptive fuzzy switching median filter

NASM:

Noise adaptive soft-switching median filter

ND:

Noise density

NFP:

Noise free pixel

NLSF-CNN:

Non local switching filter convolutional neural network

NSS:

Non-local self-similarity

PDAITMF:

Probabilistic decision based adaptive improved trimmed median filter

PDBATF:

Probabilistic decision based average trimmed filter

PDBF:

Probabilistic decision based filter

PDITMF:

Probabilistic decision based improved trimmed median filter

PDNDM:

Probabilistic decision based noise detection module

PETMF:

Patch else trimmed median filter

PSMF:

Progressive switching median filter

PSNR:

Peak signal to noise ratio

RELU:

Rectified linear unit

SMF:

Standard median filter

SSIM:

Structural similarity index

SWMF:

Switching weighted median filter

TMF:

Trimmed median filter

TSMF:

Tri-state median filter

UTMF:

Un-symmetric trimmed median filter

WMF:

Weighted median filter

∑:

Summation

μ:

Average of image

σ:

Variance of image

 = :

Equal to

 ≥ :

Greater than equal to

%:

Percentage

Z:

Clean image

Zd :

De-noised image

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    Amit Prakash Sen & Nirmal Kumar Rout

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Sen, A.P., Rout, N.K. A Comparative Analysis of the Algorithms for De-noising Images Contaminated with Impulse Noise. Sens Imaging 23, 11 (2022). https://doi.org/10.1007/s11220-022-00382-6

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