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Intensity bound limit filter for high density impulse noise removal

Journal of Ambient Intelligence and Humanized Computing volume 14, pages 12453–12475 (2023)Cite this article

Abstract

Digital images captured by electronic products are highly susceptible to salt & pepper noise during image acquisition, enrolment, preparation, and transmission phases. Therefore, it is essential to utilize superior image restoration methods to mitigate these effects. Additionally, in the restoration process, the preservation of edge data is essential as overall image quality can be severely degraded if the edge restoration processes underperform. In this paper, a novel two-stage intensity bound limit filter is proposed in which the denoised image is obtained via first stage generation of Intensity bound limit images and second stage recombination of the generated bound images. An interesting point to note is that the bound images preserve vital image edge data by extracting the infimum and supremum pixel values for any locality in the image. These separated bound images are subsequently utilized in a recombination stage to obtain the filtered image. Using this method, significant improvements in the boundary estimation are achieved especially in higher noise densities. Qualitative and quantitative analyses have been performed for standard, medical, and the Kodak image dataset which contains multiple colored images. Results show that the proposed algorithm outperforms state-of-the-art filters in terms of image detail restoration and overall noise removal. With respect to peak signal to noise ratio, an average improvement of 0.76 dB for standard images, 0.9 dB for medical images, and 1.03 db for Kodak dataset has been observed. A high-level hardware architecture has also been provided for the same.

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Abbreviations

dB:

Decibel

SAP:

Salt and pepper

PPP:

Previously processed pixel

IBL:

Intensity bound limit

BIL:

Bound image limit

FSM:

Finite state machine

RnS:

Recombination and smoothing

PSNR:

Peak signal to noise ratio

SSIM:

Structural similarity index measure

MSE:

Mean square error

ASWMF:

Adaptive switching weighted median filter

BPDM:

Based on pixel density filter

DAMF:

Dynamic adaptive median filter

FSBMMF:

Fast switching based median-mean filter

MDBUTMF:

Modified decision based unsymmetric trimmed median filter

RSIF:

Recursive cubic spline interpolation filter

SMF:

Standard median filter

SWMF:

Switching weighted median filter

TVWA:

Three-values-weighted approach

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Acknowledgements

This research was funded by University of Economics Ho Chi Minh City, Vietnam. Fund receiver: Dr. Dang Ngoc Hoang Thanh.

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    Authors and Affiliations

    1. Electronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Bhadson Road, Patiala, Punjab, 147001, India

      Piyush Satti, Vaibhav Shrotriya & Bharat Garg

    2. Department of Information Technology, College of Technology and Design, University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, 70000, Vietnam

      Dang N. H. Thanh

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    1. Piyush Satti
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    2. Vaibhav Shrotriya
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    3. Bharat Garg
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    4. Dang N. H. Thanh
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    Correspondence to Bharat Garg.

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    Satti, P., Shrotriya, V., Garg, B. et al. Intensity bound limit filter for high density impulse noise removal. J Ambient Intell Human Comput 14, 12453–12475 (2023). https://doi.org/10.1007/s12652-022-04328-4

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