Super-Resolution Reconstruction based on Tukey Norm and Adaptive Bilateral Total Variation

Abstract

In Bilateral Total Variation (BTV) regularized super-resolution reconstruction (SRR), the fidelity item is only applicable to a specific noise model, and the fixed weight of BTV regularization term cannot adapt to the changes in an image. Thus, this paper proposes a SRR algorithm based on the Tukey fidelity term and adaptive BTV regularization term. The Tukey fidelity term has a more effective outliers suppression feature to deal with complex noises, and the weight of adaptive BTV regularization term can resize itself according to the changes of image textures, which can achieve the purposes of suppressing noises and preserving edges. Experimental results show that, compared with other algorithms, the proposed algorithm has better vision effects and higher Peak Signal-to-noise Ratio (PSNR) values.

Super-resolution based image enhancement for magnetic resonance imaging

“…The super-resolution processing framework for diffusion weighted images is shown in available, for the studies in this thesis, we used L1 norm, which is known to be robust to outliers and can preserve image details [67,68].…”