Polynomial wavelet regression for images with irregular boundaries.
In this paper, we focus on denoising images for which observations are equally spaced except around the boundaries which are irregular. Such images are very common in many fields, for example in geophysics. The advantages of adding a low-order polynomial term when implementing a wavelet regression for such images are presented. Besides removing the classical restriction of having a dyadic of number of observations, this strategy reduces the bias at the edges without significantly increasing the risk. In addition, this method is simple to implement, fast and efficient. Its utility is illustrated with simulation studies and a real example.
Naveau
Philippe
P
Department of Applied Mathematics, University of Colorado, Boulder, CO 80309-0526, USA. naveau@colorado.edu
Oh
Hee-Seok
HS
D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted