Fuzzy Mapped Histogram Equalization Method for Contrast Enhancement of Remotely Sensed Images

Histogram equalisation (HE) is a widely used image contrast enhancement technique which is less computationally complex, but it fails to preserve the brightness and natural appearance of the remotely sensed images. To overcome these limitations several modifications have been reported in the literature. However, the images processed by most of the methods still suffer with the problems of saturation artifacts and un-even expansion of intensities. This paper proposes a novel fuzzy mapped HE method to overcome the aforementioned limitations by partitioning the histogram into multiple segments, expanding each segment to full dynamic range using fuzzy mapping function, then equalising each segment independently, and finally normalising the combination of equalised segments. The normalisation process is controlled by a non-negative control factor, which requires a training data for its estimation. Experimental results demonstrate that the application of proposed method in the area of remote sensing yields better results than the contemporary methods.

[1]  Abdul Ghafoor,et al.  A Framework for Outdoor RGB Image Enhancement and Dehazing , 2018, IEEE Geoscience and Remote Sensing Letters.

[2]  Gholamreza Anbarjafari,et al.  Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition , 2010, IEEE Geoscience and Remote Sensing Letters.

[3]  Lei Wu,et al.  Compensation Details-Based Injection Model for Remote Sensing Image Fusion , 2018, IEEE Geoscience and Remote Sensing Letters.

[4]  Abd. Rahman Ramli,et al.  Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation , 2003, IEEE Trans. Consumer Electron..

[5]  Nor Ashidi Mat Isa,et al.  Bi-histogram equalization using modified histogram bins , 2017, Appl. Soft Comput..

[6]  Manjunatha Mahadevappa,et al.  Brightness preserving dynamic fuzzy histogram equalization , 2010, IEEE Transactions on Consumer Electronics.

[7]  Enrique Herrera-Viedma,et al.  Fuzzy-Based Histogram Partitioning for Bi-Histogram Equalisation of Low Contrast Images , 2020, IEEE Access.

[8]  Chenghu Zhou,et al.  An Efficient Contrast Enhancement Method for Remote Sensing Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[9]  Xiangtao Zheng,et al.  Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[10]  KimYeong-Taeg Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[11]  Xiangtao Zheng,et al.  Hyperspectral Image Superresolution by Transfer Learning , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Pierre Alliez,et al.  Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[13]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[14]  Ekram Khan,et al.  Segment dependent dynamic multi-histogram equalization for image contrast enhancement , 2014, Digit. Signal Process..

[15]  Soong-Der Chen,et al.  A new image quality measure for assessment of histogram equalization-based contrast enhancement techniques , 2012, Digit. Signal Process..

[16]  Jiye Wang,et al.  Remote Sensing Image Enhancement Using Regularized-Histogram Equalization and DCT , 2015, IEEE Geoscience and Remote Sensing Letters.

[17]  Ekram Khan,et al.  Semi dynamic fuzzy histogram equalization , 2015 .