A restoration algorithm for images contaminated by mixed Gaussian plus random-valued impulse noise

Zhou, Yingyue; Ye, Zhongfu; Xiao, Yao

doi:10.1016/j.jvcir.2013.01.004

Cited by 24 publications

(18 citation statements)

References 27 publications

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“…The results are presented in Table 8, which demonstrate that PWMF is rather fast: much faster than NLMixF and even faster than TriF when the noise level is low, thanks to the simplified joint impulse factor F (k, T (k)) defined in (17). The results also show that our method is faster than Zhou [38]. which can be considered as impulse noise, to make an impartial comparison, we compute PSNR for Peppers images after removing all the four boundaries, that is with images of size 510 × 510 for Peppers512 and 254 × 254 for Peppers256 Table 3: PSNR values (dB) to remove impulse noise for TriF [18], ROLD-EPR [13], PARIGI [12], NLMixF [20] and our filter PWMF Lena p = 0.2 p = 0.3 p = 0.4 p = 0.5 Bridge p = 0.2 p = 0.3 p = 0.4 p = 0.5 Peppers256 p = 0.2 p = 0.3 p = 0.4 p = 0.5 Peppers512 p = 0.2 p = 0.3 p = 0.4 p = 0.5 Boats p = 0.2 p = 0.3 p = 0.4 p = 0.5 [18], ROLD-EPR [13], PARIGI [12] and our filter PWMF for removing impulse noise with p = 0.4 for Peppers512 Table 6: PSNR values (dB) for mixed noise removal with (Xiao) [32], (IPAMF+BM) [35], (Zhou) [38] and our filter PWMF Lena σ = 10 p = 0.1 p = 0.2 p = 0.…”

Section: Experiments and Comparisonsmentioning

confidence: 85%

“…Different papers consider different mixtures of Gaussian noise and impulse noise. We show the performance of PWMF for removing mixed noise in Tables 4, 5, 6, and 7 by comparing it with TriF [18], NLMixF [20], PA-RIGI [12] IPAMF+BM [35], Xiao [32], MNF [22], and Zhou [38]. All these comparisons show good performance of our filter except for Barbara when comparing with PARIGI.…”

Section: Experiments and Comparisonsmentioning

confidence: 95%

See 1 more Smart Citation

Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means

Liu

2015

J Sci Comput

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We first establish a law of large numbers and a convergence theorem in distribution to show the rate of convergence of the non-local means filter for removing Gaussian noise. We then introduce the notion of degree of similarity to measure the role of similarity for the non-local means filter. Based on the convergence theorems, we propose a patch-based weighted means filter for removing impulse noise and its mixture with Gaussian noise by combining the essential idea of the trilateral filter and that of the non-local means filter. Our experiments show that our filter is competitive compared to recently proposed methods.

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Section: Experiments and Comparisonsmentioning

confidence: 85%

Section: Experiments and Comparisonsmentioning

confidence: 95%

Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means

Liu

2015

J Sci Comput

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show abstract

“…Given the excellent performance of non-local methods [2,5], learned sparse models [8,18], and the combination of both [6,17] for random Gaussian noise, they were explored for impulse noise as well [20,22]. Non-local methods use redundant visual information within an image (i.e., self-similarity) to group similar image patches together, followed by collaborative filtering [2,5].…”

Section: Related Workmentioning

confidence: 99%

“…The final S can be easily estimated from the difference between the input P and the output L (see [22] …”

Section: Optimizationmentioning

confidence: 99%

Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal

Wang

Trucco

2013

2013 IEEE International Conference on Computer Vision

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“…Uniform impulse noise is considered by replacing a portion of some image pixel values with random values. Speckle noise is characterized by 2 Mathematical Problems in Engineering signal-dependent noise where the noise corrupts the image in the form of multiplicative noise [5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal

Langampol

Srisomboon

Patanavijit

et al. 2019

Mathematical Problems in Engineering

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Traditionally, several existing filters are proposed for removing a specific type of noise. However, in practice, the image communicated through the communication channel may be contaminated with more than one type of noise. Switching bilateral filter (SBF) is proposed for removing mixed noise by detecting a contaminated noise at the concerned pixel and recalculates the filter parameters. Although the filter parameters of SBF are sensitive to type and strength of noise, the traditional SBF filter has not taken the strength into account. Therefore, the traditional SBF filter cannot remove the mixed noise efficiently. In this paper, we propose a smart switching bilateral filter (SSBF) to outperform a demerit of traditional SBF filter. In the first stage of SSBF, we propose a new scheme of noise estimation using domain weight (DW) pattern which characterizes the distribution of the different intensity between a considered pixel and its neighbors. By using this estimation, the types of mixed noises and their strength are estimated accurately. The filter parameters of SBF are selected from the table where the spatial weight and radiometric weight are already learned. As a result, SSBF can improve the performance of traditional SBF and can remove mixed noises efficiently without knowing the exact type of contaminated mixed noise. Moreover, the performance of SSBF is compared to the optimal SBF filter (OSBF) where OSBF sets the optimal value of filter parameters on the contaminated mixed noise and three new filters — block-matching and 3D filtering (BM3D), nonlocal sparse representation (NCSR), and trilateral filter (TF). The simulation results showed that the performance of SSBF outperforms BM3D, NCSR, TF, and SBF and is near to optimal SBF filter, even if the SSBF does not know the type of mixed noise.

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A restoration algorithm for images contaminated by mixed Gaussian plus random-valued impulse noise

Cited by 24 publications

References 27 publications

Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means

Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means

Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal

Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal

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