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Sensors 2009, 9(11), 8669-8683; doi:10.3390/s91108669

Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

Institute of Geographic Sciences & Nature Resources Research, Chinese Academy of Sciences, Beijing, China
Author to whom correspondence should be addressed.
Received: 3 August 2009 / Revised: 10 October 2009 / Accepted: 21 October 2009 / Published: 29 October 2009
(This article belongs to the Special Issue Sensor Algorithms)
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Satellite remote sensing (RS) is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intraurban). In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolutionenhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well indetail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics.
Keywords: super-resolution reconstruction; multifractal analysis; information transfer; fractal code; gaussian upscaling super-resolution reconstruction; multifractal analysis; information transfer; fractal code; gaussian upscaling
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Hu, M.-G.; Wang, J.-F.; Ge, Y. Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis. Sensors 2009, 9, 8669-8683.

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