Higher-order total variation approaches and generalisations

Abstract

Over the last decades, the total variation (TV) evolved to one of the most broadly-used regularisation functionals for inverse problems, in particular for imaging applications. When first introduced as a regulariser, higher-order generalisations of TV were soon proposed and studied with increasing interest, which led to a variety of different approaches being available today. We review several of these approaches, discussing aspects ranging from functional-analytic foundations to regularisation theory for linear inverse problems in Banach space, and provide a unified framework concerning well-posedness and convergence for vanishing noise level for respective Tikhonov regularisation. This includes general higher orders of TV, additive and infimal-convolution multi-order total variation, total generalised variation (TGV), and beyond. Further, numerical optimisation algorithms are developed and discussed that are suitable for solving the Tikhonov minimisation problem for all presented models. Focus is laid in particular on covering the whole pipeline starting at the discretisation of the problem and ending at concrete, implementable iterative procedures. A major part of this review is finally concerned with presenting examples and applications where higher-order TV approaches turned out to be beneficial. These applications range from classical inverse problems in imaging such as denoising, deconvolution, compressed sensing, optical-flow estimation and decompression, to image reconstruction in medical imaging and beyond, including magnetic resonance imaging (MRI), computed tomography (CT), magnetic-resonance positron emission tomography (MR-PET), and electron tomography.

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• Regularization Graphs -- A unified framework for variational regularization of inverse problems
• Dyadic partition-based training schemes for TV/TGV denoising
• Boundedness and unboundedness in total variation regularization
• Total variation-based reconstruction and phase retrieval for diffraction tomography
• Stochastic EM methods with Variance Reduction for Penalised PET Reconstructions
• Data assimilation via model reference adaptation for linear and nonlinear dynamical systems