An ICI Based Algorithm for Fast Denoising of Video Signals

Lerga, Jonatan; Grbac, Edi; Sucic, Victor

doi:10.7305/automatika.2014.12.525

Cited by 12 publications

(12 citation statements)

References 10 publications

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“…These methods have been employed for color [23,24] and grayscale images [25]. Another important family of denoising methods was founded on the improved intersection of confidence intervals [27][28][29]. In [27] a fast denoising algorithm for video signals is proposed, and in [28] an adaptive denoising method for X-ray images is presented.…”

Section: Introductionmentioning

confidence: 99%

“…Another important family of denoising methods was founded on the improved intersection of confidence intervals [27][28][29]. In [27] a fast denoising algorithm for video signals is proposed, and in [28] an adaptive denoising method for X-ray images is presented. In [29] the local entropy concept is introduced for denoising and removing tissue in X-ray images.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

Arnal

Súcar

2019

Applied Sciences

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To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse and Gaussian noise, as much as mixed noise. The proposed filtering method was compared to the state-of-the-art methodologies: adaptive nearest neighbor filter, alternating projections filter, color block-matching 3D filter, fuzzy peer group averaging filter, partition-based trimmed vector median filter, trilateral filter, fuzzy wavelet shrinkage denoising filter, graph regularization filter, iterative peer group switching vector filter, peer group method, and the fuzzy vector median method. The experiments demonstrated that the introduced noise reduction technique outperforms those state-of-the-art filters with respect to the metrics peak signal to noise ratio (PSNR), the mean absolute error (MAE), and the normalized color difference (NCD).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

Arnal

Súcar

2019

Applied Sciences

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“…The confidence intervals are defined by their limits which are computed as [11,12] ( , , ) ( , , ) ( , , ) ,…”

Section: The Ici Methodsmentioning

confidence: 99%

“…The ICI algorithm calculates the values of the smallest upper and the largest lower confidence intervals limits [11,12], respectively as 1,..., 1…”

Section: The Ici Methodsmentioning

confidence: 99%

Adaptive Methods for Video Denoising Based on the ICI, FICI, and RICI Algorithms

et al. 2018

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Abstract:In various applications, reducing noise from video sequence is of crucial importance. In this paper, we have presented performance analysis of the novel video denoising method based on the relative intersection of confidence intervals (RICI) rule, and compared it to the methods based on the intersection of confidence intervals (ICI) rule and the fast ICI (FICI) rule. Detailed comparisons, based on two test video signals, are provided for a range of noise levels and different noise types. The RICI video denoising method has shown to outperform the original ICI based method, both in the algorithms execution time, reducing it by up to 11 %, and in the level of noise suppression, improving it by up to 10 dB. It also outperforms the FICI based video denoising method by up to 12.7 dB for the two test videos.

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“…and calculates the sequence of confidence intervals limits of the biased estimates for each video frame pixel independently to its left and right hand side. The confidence intervals are defined by their limits which are computed as [11,12] ( , , ) ( , , )…”

Section: The Ici Methodsmentioning

confidence: 99%

Untitled

2018

Teh. vjesn.

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In various applications, reducing noise from video sequence is of crucial importance. In this paper, we have presented performance analysis of the novel video denoising method based on the relative intersection of confidence intervals (RICI) rule, and compared it to the methods based on the intersection of confidence intervals (ICI) rule and the fast ICI (FICI) rule. Detailed comparisons, based on two test video signals, are provided for a range of noise levels and different noise types. The RICI video denoising method has shown to outperform the original ICI based method, both in the algorithms execution time, reducing it by up to 11 %, and in the level of noise suppression, improving it by up to 10 dB. It also outperforms the FICI based video denoising method by up to 12.7 dB for the two test videos.

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An ICI Based Algorithm for Fast Denoising of Video Signals

Cited by 12 publications

References 10 publications

Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

Adaptive Methods for Video Denoising Based on the ICI, FICI, and RICI Algorithms

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