Actual model. b Observation data. c Deconvolution results showing ringing artifacts. d Low-frequency wavelet coefficients LL2. e High-frequency wavelet coefficients HL1. f High-frequency wavelet coefficients HL2

Actual model. b Observation data. c Deconvolution results showing ringing artifacts. d Low-frequency wavelet coefficients LL2. e High-frequency wavelet coefficients HL1. f High-frequency wavelet coefficients HL2

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Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion based on a stationary wavelet transform (SWT) algorithm. In this paper, we discuss the ringing artifact suppression problem and anal...

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... First, when performing sharpness improvement for each sub-band, artifacts may increase, similar to the deconvolution process. Deconvolution algorithms create ringing artifacts because errors often cause strong oscillations at data discontinuities, such as edges and noise, sometimes manifesting as false edges [37]. These artifacts can distort inverse DWT images; however, if these factors are not sufficiently reflected in the SSIM and GM evaluation values, the proposed algorithm may find it difficult to derive the optimal parameters. ...
... These artifacts can distort inverse DWT images; however, if these factors are not sufficiently reflected in the SSIM and GM evaluation values, the proposed algorithm may find it difficult to derive the optimal parameters. To address this issue, a method that reduces ringing artifacts through diffusion filters [37] or PSF frequency analysis [38] can be considered as a solution. Further research is planned for the future. ...
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