Multibaseline Interferometric Phase Denoising Based on Kurtosis in the NSST Domain

Abstract

Interferometric phase filtering is a crucial step in multibaseline interferometric synthetic aperture radar (InSAR). Current multibaseline interferometric phase filtering methods mostly follow methods of single-baseline InSAR and do not bring its data superiority into full play. The joint filtering of multibaseline InSAR based on statistics is proposed in this paper. We study and analyze the fourth-order statistical quantity of interferometric phase: kurtosis. An empirical assumption that the kurtosis of interferograms with different baselines keeps constant is proposed and is named as the baseline-invariant property of kurtosis in this paper. Some numerical experiments and rational analyses confirm its validity and universality. The noise level estimation of nature images is extended to multibaseline InSAR by dint of the baseline-invariant property of kurtosis. A filtering method based on the non-subsampled shearlet transform (NSST) and Wiener filter with estimated noise variance is proposed then. Firstly, multi-scaled and multi-directional coefficients of interferograms are obtained by NSST. Secondly, the noise variance is represented as the solution of a constrained non-convex optimization problem. A pre-thresholded Wiener filtering with estimated noise variance is employed for shrinking or zeroing NSST coefficients. Finally, the inverse NSST is utilized to obtain the filtered interferograms. Experiments on simulated and real data show that the proposed method has excellent comprehensive performance and is superior to conventional single-baseline filtering methods.

Noise Suppression and Edge Preservation for Low-Dose COVID-19 CT Images Using NLM and Method Noise Thresholding in Shearlet Domain

“…Figure 5 shows the denoised output from all eight different frameworks when applied on the first image from Figure 1 , respectively. Similarly, Figure 6 , Figure 7 and Figure 8 show the results of the denoising process for the case of images 2, 3, and 4 from Figure 2 as outputted by the frameworks [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], respectively.…”

“…This leaves us to use low-dose X-rays for CT scans, which give us scans corroded by noise and disturbance. Thus, deliberating the above, the use of high-dose X-rays may create further complications for a patient who may or may not be infected with coronavirus [ 16 , 17 , 18 ]. Conclusively, the only way to prevent patients from side effects consistent with high-dose X-rays while diagnosing the presence of coronavirus early is using low-dose X-rays to generate CT scans, then implementing a robust denoising algorithm to the scans [ 19 , 20 , 21 , 22 , 23 ].…”

An effective nonlocal means image denoising framework based on non-subsampled shearlet transform

“…In multi-baseline InSAR (interferometric synthetic aperture radar), interferometric phase filtering is a critical step. Multi-baseline interferometric phase filtering methods primarily follow single baseline INSAR approaches and which do not fully exploit its data supremacy is being proposed in [42] i.e. statistically-based joint filtering of multi-baseline InSAR.…”

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

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