Remote Sensing Image Super-resolution Based on Sparse Representation

Study on the Super-resolution Reconstruction Algorithm for Remote Sensing Image Based on Compressed Sensing

Image super resolution reconstruction has important significance in remote sensing image feature extraction and classification etc.. Because the remote sensing image size is larger, it is difficult to super resolution reconstruction using multiple images, the compressed sensing (CS) theory was introduced into the super-resolution reconstruction. Algorithm designed the low pass filter to reduce ...

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...

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...