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

Abstract

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.

Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

“…In 43 the Robust Outlyingness Ratio, which is a local statistic capable of efficiently detecting outliers, was used to remove impulsive noise disturbances and then the NLM filter was applied, adapting its parameters to the remaining noise characteristic. In 44 the impulses were first detected by sorted quadrant median vector 28 and the unfiltered noise was smoothed out by BF, which parameters were tuned on the basis of an estimation of the mixture noise composition and its intensity. The method of mixed noise reduction described in 45 firstly detects the impulses and the subsequent stages use filters utilizing the PCA technique.…”

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

“…Hence, they proposed a trilateral smoothing algorithm to solve this challenge. Langampol, et al [104] suggested to improve the performance of switching bilateral filter by introducing a domain weight pattern.…”

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“…However, it is more difficult to remove mixed noise due to the complex distribution of the noise. Researchers have developed many effective algorithms [23–32]. Xiao et al.…”

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