Curvelet transform based adaptive image deblocking method

Jin, Jing; Yuan, Jie; Shen, Qinghong; Yu, Yao; Zhou, Yu; Wang, Yuanqing

doi:10.1016/j.compeleceng.2014.03.010

Cited by 2 publications

(1 citation statement)

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“…The detail layer is an intermediate one, and it contains intermediate-frequency coefficients. The fine layer is the outer layer (high-frequency layer), and it provides edge and detail information of the original image [24]. The low-frequency coefficients are less contaminated by noise, but high-frequency coefficients are highly contaminated by noise.…”

Section: Curvelet Transform Via Wrapping Techniquementioning

confidence: 99%

Enhancement of SAR images using fuzzy shrinkage technique in curvelet domain

Math

Kaliyaperumal

2017

Sādhanā

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The synthetic aperture radar (SAR) images are mainly affected by speckle noise. Speckle degrades the features in the image and reduces the ability of a human observer to resolve fine detail, hence despeckling is very much required for SAR images. This paper presents speckle noise reduction in SAR images using a combination of curvelet and fuzzy logic technique to restore speckle-affected images. This method overcomes the limitation of discontinuity in hard threshold and permanent deviation in soft threshold. First, it decomposes noise image into different frequency scales using curvelet transform, and then applies the fuzzy shrinking technique to high-frequency coefficients to restore noise-contaminated coefficients. The proposed method does not use threshold approach only by proper selection of shrinking parameter the speckle in SAR image is suppressed. The experiment is carried out on different resolutions of RISAT-1 SAR images, and results are compared with the existing filtering algorithms in terms of noise mean variance (NMV), mean square difference (MSD), equal number of looks (ENL), noise standard deviation (NSD) and speckle suppression index (SSI). A comparison of the results shows that the proposed technique suppresses noise significantly, preserves the details of the image and improves the visual quality of the image.

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Section: Curvelet Transform Via Wrapping Techniquementioning

confidence: 99%