Skip to main navigation
Skip to search
Skip to main content
NYU Scholars Home
Help & FAQ
Home
Profiles
Research units
Research output
Search by expertise, name or affiliation
Image denoising via adjustment of wavelet coefficient magnitude correlation
J. Portilla,
E. P. Simoncelli
Neural Science
Research output
:
Contribution to conference
›
Paper
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Image denoising via adjustment of wavelet coefficient magnitude correlation'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Additive White Gaussian Noise
50%
Autocorrelation
100%
Complex Wavelets
50%
Denoising
50%
Fewer Iterations
50%
Image Denoising
100%
Magnitude Correlation
100%
Mean Squared Error
50%
Natural Images
50%
Noise Free
50%
Noisy Image
50%
Photographic Images
50%
Statistical Properties
50%
Wavelet Coefficients
100%
Wavelet Decomposition
50%
Wavelet Subbands
50%
Engineering
Complex Wavelet
100%
Mean-Squared-Error
100%
Natural Image
100%
Noisy Image
100%
Photographic Image
100%
Statistical Property
100%
Wavelet Decomposition
100%
Computer Science
Complex Wavelet
50%
de-noising
50%
image denoising
100%
Magnitude Correlation
100%
Photographic Image
50%
Statistical Property
50%
Wavelet Coefficient
100%
Wavelet Decomposition
50%