Statistical imaging and complexity regularization

We apply the complexity–regularization principle to statistical ill-posed inverse problems in imaging. The class of problems studied includes restoration of images corrupted by Gaussian or Poisson noise and nonlinear transforms. We formulate a natural distortion measure in image space and develop nonasymptotic bounds on estimation performance in terms of an… CONTINUE READING