Remote Sensing Image Enhancement Based on Non-subsampled Shearlet Transform and Parameterized Logarithmic Image Processing Model

doi:10.11947/j.AGCS.2015.20140466

Abstract

Abstract:

Aiming at parts of remote sensing images with dark brightness and low contrast, a remote sensing image enhancement method based on non-subsampled Shearlet transform and parameterized logarithmic image processing model is proposed in this paper to improve the visual effects and interpretability of remote sensing images. Firstly, a remote sensing image is decomposed into a low-frequency component and high frequency components by non-subsampled Shearlet transform.Then the low frequency component is enhanced according to PLIP (parameterized logarithmic image processing) model, which can improve the contrast of image, while the improved fuzzy enhancement method is used to enhance the high frequency components in order to highlight the information of edges and details. A large number of experimental results show that, compared with five kinds of image enhancement methods such as bidirectional histogram equalization method, the method based on stationary wavelet transform and the method based on non-subsampled contourlet transform, the proposed method has advantages in both subjective visual effects and objective quantitative evaluation indexes such as contrast and definition, which can more effectively improve the contrast of remote sensing image and enhance edges and texture details with better visual effects.

Key words: remote sensing image, image enhancement, non-subsampled Shearlet transform(NSST), parameters logarithmic image processing (PLIP) model, fuzzy enhancement

CLC Number:

P237

TAO Feixiang, WU Yiquan. Remote Sensing Image Enhancement Based on Non-subsampled Shearlet Transform and Parameterized Logarithmic Image Processing Model[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(8): 884-892.

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