A noise-suppressing and edge-preserving multiframe super-resolution image reconstruction method

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dc.contributor.authorMaiseli, Baraka J.
dc.contributor.authorAlly, Nassor
dc.contributor.authorGao, Huijun
dc.date.accessioned2019-08-03T18:23:49Z
dc.date.available2019-08-03T18:23:49Z
dc.date.issued2015-03-12
dc.description.abstractSuper-resolution technology is an approach that helps to restore high quality images and videos from degraded ones. The method stems from an ill-posed minimization problem, which is usually solved using the L2 norm and some regularization techniques. Most of the classical regularizing functionals are based on the Total Variation and the Perona–Malik frameworks, which suffer from undesirable artifacts (blocking and staircasing). To address these problems, we have developed a super-resolution framework that integrates an adaptive diffusion-based regularizer. The model is feature-dependent: linear isotropic in flat regions, a condition that regularizes an image uniformly and removes noise more effectively; and nonlinear anisotropic near boundaries, which helps to preserve important image features, such as edges and contours. Additionally, the new regularizing kernel incorporates a shape-defining parameter that can be automatically updated to ensure convexity and stability of the corresponding energy functional. Comparisons with other methods show that our method is superior and, more importantly, can achieve higher reconstruction factors.en_US
dc.identifier.doi10.1016/j.image.2015.03.001
dc.identifier.urihttp://hdl.handle.net/20.500.11810/5302
dc.language.isoen_USen_US
dc.publisherSignal Processing: Image Communicationen_US
dc.subjectConvex Optimization, Super-Resolution, Backward Diffusion, Regularizationen_US
dc.titleA noise-suppressing and edge-preserving multiframe super-resolution image reconstruction methoden_US
dc.typeJournal Articleen_US
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