The switching bilateral denoising performance influence of spatial and radiometric variance

Langampol, Kriengkri; Lee, Wilaiporn; Patanavijit, Vorapoj

doi:10.1109/ecticon.2016.7561285

Cited by 2 publications

(6 citation statements)

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“…On the other hand, bilateral filter [15][16][17] also has been extensively studied and modified for mixed noise removal. Trilateral filter (TF) [18][19][20][21], switching bilateral filter (SBF) [22][23][24][25], and adaptive bilateral filter (ABF) [34] are the modification of traditional bilateral filter. TF filter performs a noise removal based on a hybrid scheme where traditional BF and Rank-Ordered Absolute Differences (ROAD) statistic are exploited.…”

Section: Related Workmentioning

confidence: 99%

“…Tk1=30 and Tk2=15 are thresholds for identifying nonlinear noise and linear noise. As described in [22], the appropriated threshold of Tk1 and Tk2 is within the range of [25,30] and [5,10,15], respectively. Therefore, in this work, the values Tk1 and Tk2 are obtained from the experimental study using 10 images.…”

Section: Switching Bilateral Filtermentioning

confidence: 99%

“…However, only using NLM cannot achieve high performance on local patches grouping under high strength of the noise. On the other hand, noise removal techniques based on the bilateral filter (BF) [15][16][17]-trilateral filter (TF) [18][19][20][21] and switching bilateral filter (SBF) [22][23][24][25]-are proposed. These techniques are proposed to improve the performance of BF under impulsive noise.…”

Section: Introductionmentioning

confidence: 99%

“…SBF determines the type of contaminated noise using the sorted quadrant median vector (SQMV) technique and reference median (RM) technique [22]. Then, it switches the predetermined filter parameters-spatial weight ( S ) and radiometric weight ( R )-on the detected type of noise [25]. In our previous work [25], we evaluated the performance of SBF filter for several types of images, noise types, and noise strength.…”

Section: Introductionmentioning

confidence: 99%

“…Then, it switches the predetermined filter parameters-spatial weight ( S ) and radiometric weight ( R )-on the detected type of noise [25]. In our previous work [25], we evaluated the performance of SBF filter for several types of images, noise types, and noise strength. Although switching bilateral filter (SBF) has merit to remove both linear and nonlinear noise, its demerit is the sensitivity of S and R which cannot be fixed and needs to be re-calculated on the detected type and level of noise.…”

Section: Introductionmentioning

confidence: 99%

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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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