期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 56, 期 4, 页码 2312-2327出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2017.2778191
关键词
Fast adaptive shrinkage-thresholding algorithm (FASTA); global joint dictionary model (GJDM); remote sensing images; sparse representation; super-resolution (SR)
类别
资金
- National Natural Science Foundation of China [61671350, 61173090, 61072106, 61271302]
- National Research Foundation for the Doctoral Program of Higher Education of China [20130203110009]
- National Basic Research Program (973 Program) of China [2013CB329402]
Sparse representation has been widely used in the field of remote sensing image super-resolution (SR) to restore a high-quality image from a low-resolution (LR) image, e.g., from the blurred and downsampled version of an LR image's high-resolution (HR) counterpart. It is well known that each image patch can be represented by a linear combination of the atoms of an overcomplete dictionary, and we can obtain an expression of sparse coefficients by l(1) norm regularization. Owing to the lack of an inner relationship between image patches and an image's global information, the traditional methods of jointly training two overcomplete dictionaries cannot obtain good SR results. Therefore, we propose an effective approach for remote sensing image SR based on sparse representation. More specifically, a novel global joint dictionary model (GJDM) is used to explore the prior knowledge of images, including local and global characteristics. First, we train two dictionaries for detail image patches and HR patches. Second, in order to enhance the inner relationship between image patches, we introduce a global self-compatibility model for global regularization. Finally, the sparse representation and the local and nonlocal constraints are integrated to improve the performance of the model, and the fast adaptive shrinkage-thresholding algorithm is employed to solve the convex optimization problem in the GJDM. Compared with other methods, the results of the proposed method show good SR performance in preserving details and texture information and significant improvement in a peak signal-to-noise ratio.
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