Image Super-Resolution Reconstruction via EROMP Sparse Representation

Super-Resolution Image Reconstruction with Improved Sparse Representation

In this paper, we present a new approach to reconstruct a high resolution (HR) image from a low resolution (LR) input image based on a two dimensional (2D) sparse method. The new method consists of three phases. Firstly, the nonlinear feature of the input LR image is divided into the linear subspace, and then LR-HR dictionaries are learned to reduce the blurred artifacts of the image. Secondly,...

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Multi-morphology image super-resolution via sparse representation

A novel multi-morphology image super-resolution algorithm via sparse representation is proposed in this paper. Firstly, the observed low resolution image is decomposed into a linear combination of morphological components by morphological component analysis. Secondly, for effective representation of different components, each dictionary pair is learned from the low resolution component and comp...

Image Classification via Sparse Representation and Subspace Alignment

Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...

Simultaneous image fusion and super-resolution using sparse representation

1566-2535/$ see front matter 2012 Elsevier B.V. A doi:10.1016/j.inffus.2012.01.008 ⇑ Corresponding author. Tel.: +86 731 88822924. E-mail addresses: [email protected] (H. Yin), sh [email protected] (L. Fang). Given multiple source images of the same scene, image fusion integrates the inherent complementary information into one single image, and thus provides a more complete and accurate de...