Non-smooth Hybrid Energy Functional Regularization Model for Image Reconstruction

For overcoming the shortcomings of total variation for image reconstruction, which easily smooth image texture, and produce lots of artificial edges, a non-smooth hybrid energy functional regularization model and iterative algorithm is proposed. Firstly, fitting term is described by L1 norm for blurred image by system and salt and pepper noise, regularization terms are described by fractional order bounded variation function semi-norm and L1 norm. Secondly, resorting to introduce auxiliary variables, the primal non-smooth energy functional regularization model without condition constraints is converted into energy functional regularization model with condition constraints, which is split into six easily computing sub-problems by the alternating direction method of multipliers (ADMM). Finally, alternating iterative six sub-problems, an improved image reconstruction algorithm is proposed. Numerical experiments show that the proposed model has advantages over several state-of-art approaches in terms of the reconstruction visual effect.

Abstract licence: CC BY

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