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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Availability of data and materials
Salt and pepper
Previously processed pixel
Intensity bound limit
Bound image limit
Finite state machine
Recombination and smoothing
Peak signal to noise ratio
Structural similarity index measure
Mean square error
Adaptive switching weighted median filter
Based on pixel density filter
Dynamic adaptive median filter
Fast switching based median-mean filter
Modified decision based unsymmetric trimmed median filter
Recursive cubic spline interpolation filter
Standard median filter
Switching weighted median filter
Appiah O, Asante M, Hayfron-Acquah JB (2022) Improved approximated median filter algorithm for real-time computer vision applications. J King Saud Univ-Comput Inf Sci 34:782–792
Balasubramanian G, Chilambuchelvan A, Vijayan S, Gowrison G (2016) An extremely fast adaptive high-performance filter to remove salt and pepper noise using overlapping medians in images. Imaging Sci J 64(5):241–252
Chen J, Li F (2019) Denoising convolutional neural network with mask for salt and pepper noise. IET Image Proc 13(13):2604–2613
Eng H-L, Ma K-K (2001) Noise adaptive soft-switching median filter. IEEE Trans Image Process 10(2):242–251
Erkan U, GöKREM L (2018) A new method based on pixel density in salt and pepper noise removal. Turk J Electric Eng Comput Sci 26(1):162–171
Erkan U, Enginoğlu S, Thanh DN (2019) Adaptive frequency median filter for the salt and pepper denoising problem. IET Image Proc 14(7):1291–1302
Esakkirajan S, Veerakumar T, Subramanyam AN, PremChand C (2011) Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Process Lett 18(5):287–290
Faragallah OS, Ibrahem HM (2016) Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU-Int J Electron Commun 70(8):1034–1040
Kumar SV, Nagaraju C (2021) Support vector neural network based fuzzy hybrid filter for impulse noise identification and removal from gray-scale image. J King Saud Univ-Comput Inf Sci 33:820–835
Lu C-T, Chen Y-Y, Wang L-L, Chang C-F (2016) Removal of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size window. Pattern Recogn Lett 80:188–199
Nair MS, Revathy K, Tatavarti R (2008) An improved decision-based algorithm for impulse noise removal. In: 2008 Congress on Image and Signal Processing, vol. 1: pp 426–431
Ng P-E, Ma K-K (2006) A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans Image Process 15(6):1506–1516
Patel P, Majhi B, Jena B, Tripathy C (2012) Dynamic adaptive median filter (damf) for removal of high density impulse noise. Int J Image Graph Signal Process 4(11):53
Satti P, Sharma N, Garg B (2020) Min-max average pooling based filter for impulse noise removal. IEEE Signal Process Lett 27:1475–1479
Thanh DN, Prasath VS, Phung TK, Hung NQ (2020a) Impulse denoising based on noise accumulation and harmonic analysis techniques. Optik 241:166163
Thanh DNH, Hien NN, Prasath S (2020b) Adaptive total variation l1 regularization for salt and pepper image denoising. Optik 208:163677
Veerakumar T, Esakkirajan S, Vennila I (2014) Recursive cubic spline interpolation filter approach for the removal of high density salt-and-pepper noise. SIViP 8(1):159–168
Vijaykumar V, Vanathi P, Kanagasabapathy P, Ebenezer D (2008) High density impulse noise removal using robust estimation based filter. IAENG Int J Comput Sci 35(3):1–8
Vijaykumar V, Mari GS, Ebenezer D (2014) Fast switching based median-mean filter for high density salt and pepper noise removal. AEU-Int J Electron Commun 68(12):1145–1155
Wang Z, Zhang D (1999) Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans Circ Syst II Analog Digit Signal Process 46(1):78–80
Zhang S, Karim MA (2002) A new impulse detector for switching median filters. IEEE Signal Process Lett 9(11):360–363
This research was funded by University of Economics Ho Chi Minh City, Vietnam. Fund receiver: Dr. Dang Ngoc Hoang Thanh.
Conflict of interest
Consent to participate
Consent for publication
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
All authors have contributed equally.
About this article
Cite this article
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