We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform [3] and the curvelet transform [7, 6]. Our implementations offer...
Regularization techniques have been in use in signal recovery for over four decades. In this paper, we propose a new, synthetic approach to the study of regularization methods in ...
Sparse Orthonormal Transforms (SOT) has recently been proposed as a data compression method that can achieve sparser representations in transform domain. Given initial conditions,...
In this paper, we present a new deconvolution method, able to deal with noninvertible blurring functions. To avoid noise amplification, a prior model of the image to be reconstruc...
We develop a new approach to image denoising based on complexity regularization. This technique presents a flexible alternative to the more conventional l2 , l1 , and Besov regula...