Combination of adaptive vector median filter and weighted mean filter for removal of high‐density impulse noise from colour images

Roy, Amarjit; Singha, Joyeeta; Manam, Lalit; Laskar, Rabul Hussain

doi:10.1049/iet-ipr.2016.0320

Cited by 68 publications

(26 citation statements)

References 27 publications

(42 reference statements)

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“…Moreover the number of clock given for single analysis would be efficiently utilized. In [1] this architecture window of order 36.…”

Section: Partitoned 6x6 Median Filteringmentioning

confidence: 99%

Architectural Design of Effective Edge Preserving Median Filter with Error Correction System

2019

IJRTE

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Designing of Median filter that can process 36 pixels at a time with edge preservation similar to a filter of size 9. Median sorting is done using Modified minimum exchange sorting method which attracts double the amount of inputs in order to reduce number of comparators used for median filtering. For the same reason i.e. double the amount of inputs switching loss is high in the circuit therefore data driven clock gating (DDCG) is applied for SRAM to form data driven FIFO. Considering space radiation that could excite memory state, Addition of DMR (Double Modular Redundancy) in FPIC would rectify the soft error that could possibly occur due to radiation in space. Therefore proposed method is capable of producing sharp image, controlling switching loss, minimizes area, and reduces soft errors.

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“…Moreover the number of clock given for single analysis would be efficiently utilized. In [1] this architecture window of order 36.…”

Section: Partitoned 6x6 Median Filteringmentioning

confidence: 99%

Architectural Design of Effective Edge Preserving Median Filter with Error Correction System

2019

IJRTE

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“…It applied different type of filters for different kind of noise in that image. Roy et al explained the combined noise removing technique to remove the high density impulse noise from the colour image. In that work first the noisy image and non‐ noisy images are separated and applied a combined adaptive vector median filter and weighted mean filter to improve PSNR.…”

Section: Related Workmentioning

confidence: 99%

Sparse data encoder and decoder to improve security in video steganography

Perumal

Muthusamy²,

Gengavel³

2018

Concurrency and Computation

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Summary Steganography is a technological art to hide text, audio, digital image,s and videos in secret communication, and in the past decade, much research has been carried out the respective field. Currently, secret communications through video steganography are focused on numerous applications due to emerge of social communication networks such as whatsApp, twitter, and Facebook. In video‐based steganography, the video with any format have been separated into number of frames associated to the dividing speed and select few frames for embedding purpose. The existing video steganography techniques have some advantages and limitations in information hiding and so to design a well‐intentioned steganography algorithm for improving security and robustness are significant task. The experimental results show that the proposed block‐based transform domain sparse data encoder and decoder video steganography method provide high security with improved PSNR.

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“…But, the computational complexity was high. A combination of adaptive Vector Median Filter (VMF) and weighted mean filter [11] was proposed to remove high-density impulse noise from color images. In this approach, the noisy and non-noisy pixels were classified according to the non-causal linear prediction error.…”

Section: Literature Surveymentioning

confidence: 99%

A sporadic decomposition of Hankel structured matrix in logarithmic and wavelet domain for impulse noise removal

Victoria.L¹,

Sathappan

2018

IJET

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Noise removal from the color images is the most significant and challenging task in image processing. Among different conventional filter methods, a robust Annihilating filter-based Low-rank Hankel matrix (r-ALOHA) approach was proposed as an impulse noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix to decompose the sparse impulse noise components from an original image. However, in this algorithm, the patch image was considered as it was sparse in the Fourier domain only. It requires an analysis of noise removal performance by considering the other transform domains. Hence in this article, the r-ALOHA can be extended into other transform domains such as log and exponential. In the log and exponential domain, the logarithmic and exponential functions are used for modeling the multiplicative noise model. But, this model is used only for positive outcomes. Therefore, wavelet transform domain is applied to the noise model that localizes an image pixel in both frequency and time domain simultaneously. Moreover, it separates the most vital information in a given image. Thus, it is feasible for obtaining a better approximation of the considered function using few coefficients. Finally, the experimental results show the performance effectiveness of the proposed algorithm.

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Combination of adaptive vector median filter and weighted mean filter for removal of high‐density impulse noise from colour images

Cited by 68 publications

References 27 publications

Architectural Design of Effective Edge Preserving Median Filter with Error Correction System

Architectural Design of Effective Edge Preserving Median Filter with Error Correction System

Sparse data encoder and decoder to improve security in video steganography

A sporadic decomposition of Hankel structured matrix in logarithmic and wavelet domain for impulse noise removal

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