In the context of shape and image modeling by manifold learning, we focus on the problem of denoising. A set of shapes or images being known through given samples, we capture its s...
Existing wavelet-based image denoising techniques all assume a probability model of wavelet coefficients that has zero mean, such as zero-mean Laplacian, Gaussian, or generalized ...
Many computer vision problems can be formulated in
a Bayesian framework with Markov Random Field (MRF)
or Conditional Random Field (CRF) priors. Usually, the
model assumes that ...
In this paper we propose an algorithm for image recovery where completely lost blocks in an image/video-frame are recovered using spatial information surrounding these blocks. Our...
The developments in wavelet theory have given rise to the wavelet thresholding method, for extracting a signal from noisy data [1,2]. Multiwavelets, wavelets with several scaling ...