Apr 27, 2018 · This study proposes a novel super‐resolution regularisation model based on adaptive sparse representation and self‐learning frameworks.
This study proposes a novel super-resolution regularisation model based on adaptive sparse representation and self-learning frameworks.
In this section, we provide a general view of sparse representation, image SR via sparse representation and RPCA which are closely related with our research.
This study proposes a novel super-resolution regularisation model based on adaptive sparse representation and self-learning frameworks that achieves better ...
Existing methods for image super-resolution (SR) usually use ℓ1-regularization and ℓ2-regularization to emphasize the sparsity and the correlation, ...
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This study proposes a novel super-resolution regularisation model based on adaptive sparse representation and self-learning frameworks that achieves better ...
Abstract: sparse representation has been used as a powerful statistical image modelling technique in single image super resolution (SISR).
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In this paper we propose an adaptive sparse domain selection (ASDS) scheme for sparse representation. By learning a set of compact sub-dictionaries from high ...
A new super-resolution model based on sparsity regularization in Bayesian framework is presented. The fidelity term restricts the underlying image to be ...
In this paper, we propose an adaptive dictionary learning and double ℓ 1 regularized sparse-representation model for HSI SR. In particular, this novel model ...