.png){kind=icon}
Return
Image denoising using derotated complex wavelet coefficients
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2008.926146.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A method for removing additive Gaussian noise from digital images is described. It is based on statistical modeling of the coefficients or a redundant oriented, complex multiscale transform. Two types of modeling are used to model the wavelet coefficients. Both are based on Gaussian scale mixture (GSM) modeling of neighborhoods of coefficients at adjacent locations and scales. Modeling of edge and ridge discontinuities is performed using wavelet coefficients derotated by twice the phase of the coefficient at the same location and the next coarser scale. Other areas are modeled using standard wavelet coefficients. An adaptive Bayesian model selection framework is used to determine the modeling applied to each neighborhood. The proposed algorithm succeeds in providing improved denoising performance at structural image features. reducing ringing artifacts and enhancing sharpness, while avoiding degradation in other areas. The method outperforms previously published methods visually and in standard tests.
Keywords:
complex
denoising
image
interscale phase
restoration
wavelet
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSave
ShareSaveShareSave
View more