Combining sparse coding with structured output regression machine for single image super-resolution

In this paper, considering that the trained dictionary pairs by sparse coding based super-resolution (SR) methods have difficulty capturing the complicated nonlinear relationships between the low-resolution (LR) and high-resolution (HR) feature spaces, we propose a new single image SR method by combining sparse coding with the improved structured output regression machine (SORM). In the proposed method, the dictionary pairs are firstly learned by joint sparse coding to characterize the structural domain of each feature space and add more consistency between the sparse codes of two feature spaces. Then, since the classical SORM does not give sufficient weight to the independence of different output components, we improve the SORM by considering the correlation and independence between different output components to establish a set of mapping functions for tying the sparse code of two feature spaces. With this, the more precise mapping relationships between two feature spaces are obtained by the trained dictionary pairs and mapping functions. Moreover, we propose a new global and nonlocal optimization for further enhancing the quality of the restored HR images. Extensive experiments validate that the proposed method can achieve convincing improvement over other state-of-the-art methods in terms of the reconstruction quality and computational cost.