Efficient Nonlocal Linear Image Denoising: Bilevel Optimization with Nonequispaced Fast Fourier Transform and Matrix-Free Preconditioning

We present a new approach for nonlocal image denoising, based around the application of an unnormalized extended Gaussian analysis of variance kernel within a bilevel optimization algorithm. A critical bottleneck when solving such problems for finely resolved images is the solution of huge-scale, dense linear systems arising from the minimization of an energy term. We tackle this using a Krylov subspace approach, with a nonequispaced fast Fourier transform utilized to approximate matrix-vector products in a matrix-free manner. We accelerate the algorithm using a novel change-of-basis approach to account for the (known) smallest eigenvalue-eigenvector pair of the matrices involved, coupled with a simple but frequently very effective diagonal preconditioning approach. We present a number of theoretical results concerning the eigenvalues and predicted convergence behavior and a range of numerical experiments which validate our solvers and use them to tackle parameter learning problems. These demonstrate that very large problems may be effectively and rapidly denoised with very low storage requirements on a computer.