.png){kind=icon}
.png){kind=icon}
Return
Non-consistent cell-average multiresolution operators with application to image processing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2015.08.074.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In recent years different techniques to process signal and image have been designed and developed. In particular, multiresolution representations of data have been studied and used successfully for several applications such as compression, denoising or inpainting. A general framework about multiresolution representation has been presented by Harten (1996) [20]. Harten's schemes are based on two operators: decimation, P. and prediction, P. that satisfy the consistency property DP _ I, where I is the identity operator. Recently, some new classes of multiresolution operators have been designed using learning statistical tools and weighted local polynomial regression methods obtaining filters that do not satisfy this condition. We show some proposals to solve the consistency problem and analyze its properties. Moreover, some numerical experiments comparing our methods with the classical methods are presented. (C) 2015 Elsevier Inc. All rights reserved.
Keywords:
Generalized wavelets
Consistency
Subdivision schemes
Statistical multiresolution
Image compression
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.4
Papers:
2.3W
Citations:
3.3W