Remote Sensing Image Enhancement Method Based on Multi-scale Retinex

Henan, Wu; Yang, Guang; Xu, Zhonglin; Li, Dejun; Yuan, Yixuan

doi:10.1109/icm.2011.323

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…In this experiment, the proposed method is compared with the histogram equalization method (HE), the CT algorithm (Nezhadarya and Shamsollahi, 2006), the multiscale retinex algorithm (MSR) (Henan et al, 2011), the fuzzy contrast enhancement based on NSCT (NSCT-FU) (Men et al, 2010) and the unsharp masking method based on NSCT (NSCT-UM) (Pu et al, 2014). For the CT and NSCT, we use eight, 16 and 16 directions in the scales.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

Liu

Jia

Yang

et al. 2016

Transactions of the Institute of Measurement and Control

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The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

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