Multiscale Decompositions and Optimization

In this thesis, the following type Tikhonov regularization problem will be systematically studied: (ut, vt) := argmin u+v=f {‖v‖X + t‖u‖Y }, where Y is a smooth space such as a BV space or a Sobolev space and X is the space in which we measure distortion. Examples of the above problem occur in denoising in image processing, in numerically treating inverse problems, and in the sparse recovery problem of compressed sensing. It is also at the heart of interpolation of linear operators by the real method of interpolation. We shall characterize the minimizing pair (ut, vt) for (X,Y ) := (L2(Ω),BV(Ω)) as a primary example and generalize Yves Meyer’s result in [11] and Antonin Chambolle’s result in [6]. After that, the following multiscale decomposition scheme will be studied: uk+1 := argmin u∈BV(Ω)∩L2(Ω) { 2 ‖f − u‖2L2 + tk|u− uk|BV}, where u0 = 0 and Ω is a bounded Lipschitz domain in R d. This method was introduced by Eitan Tadmor et al. and we shall improve the L2 convergence result in [16]. Other pairs such as (X,Y ) := (Lp,W (Lτ )) and (X,Y ) := (l2, lp) will also be mentioned. In the end, the numerical implementation for (X,Y ) := (L2(Ω),BV(Ω)) and the corresponding convergence results will be given.