Abstract
Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several constant dictionaries learned from external databases. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which uses group as the basic unit, and trains dictionary with external database and the input low-resolution image itself for each group to ensure that the dictionary is suitable for the patches in the group. Simultaneous sparse coding algorithm is used to accelerate the processing and improve the result. Extensive experiments on natural images show that our method achieves better results than some state-of-the-art algorithms in terms of both objective and human visual evaluations.
Highlights
Super-resolution (SR) is the method that uses one or several low-resolution (LR) images to reconstruct a highresolution (HR) image
Where J(X) is a regularization term specifying the prior knowledge of the HR image and λ is a scalar balancing between the quadratic fidelity term and the regularization term, such as the total variation (TV) regularization [6], edge smoothness [7], and gradient profile priors [8]
For methods based on fixed external dictionaries, we choose the work of Yang et al [20] for comparison; for methods based on non-local similarity, we choose the work of Glasner et al [15] for comparison; for methods that combine sparse representation and nonlocal similarity, we choose the representative work ASDS method [13] for comparison
Summary
Super-resolution (SR) is the method that uses one or several low-resolution (LR) images to reconstruct a highresolution (HR) image. Zhang et al [18] and Zhang et al [19] exploit the concept of group-based sparse representation for general image inverse problem and develop an efficient and effective algorithm for image restoration and image compressive sensing recovery. Inspired by these works, this paper uses group as the basic unit for image super-resolution. The main contribution of our proposed method is that we divide the input image into several groups to combine the sparsity and the self-similarity of natural images in a unified framework and improve the performance of the dictionary for each group with the novel online dictionary learning method, which is more suitable than the one trained with classic algorithms.
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