Super-resolution Methods Based on Dictionary Learning for Remote Sensing Images

doi:10.13474/j.cnki.11-2246.2017.0182

Abstract

Abstract: In recent years, super-resolution reconstruction technology based on dictionary learning has obtained much attention and has been intensively studied. Compared with the super-resolution method based on reconstruction, the learning-based method makes full use of prior knowledge. This learning-based method can get better results when magnification is high, which has been recognized as an extremely promising method. The properties of the existed learning-based super-resolution reconstruction algorithms are firstly analyzed systematically. Then this paper reviews the theory of three learning-based algorithms and combs their advantages and disadvantages. Finally, according to characteristics of remote sensing image, the same data sources are used for dictionary learning. We select these three algorithms mentioned to generate high and low resolution joint dictionary and adopt test images of different sizes and zoom and complete reconstruction. The reconstruction performance, robustness and complexity of various algorithms are analyzed comprehensively by experimental results. What's more, aiming at different application requirements of remote sensing image, the applicability of different algorithms is further studied.

Key words: super-resolution reconstruction, sparse representation, remote sensing imagery, dictionary learning

CLC Number:

P237

YANG Zhenyin, SUI Lichun, LI Li, KANG Junmei, DING Mingtao. Super-resolution Methods Based on Dictionary Learning for Remote Sensing Images[J]. 测绘通报, 2017, 0(6): 21-25.

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