Remote Sensing Image Enhancement using Regularized-Histogram Equalization and DCT


Abstract

In this letter, an effective enhancement method for remote sensing images is introduced to improve the global contrast and the local details. The proposed method constitutes an empirical approach by using the regularized-histogram equalization and the discrete cosine transform (DCT) to improve the image quality. Firstly, a new global contrast enhancement method by regularizing the input histogram is introduced. More specifically, this technique uses the sigmoid function and the histogram to generate a distribution function for the input image. The distribution function is then used to produce a new image with improved global contrast by adopting the standard lookup table-based histogram equalization technique. Secondly, the discrete cosine transform coefficients of the previous contrast improved image are automatically adjusted to further enhance the local details of the image. Compared with conventional methods, the proposed method can generate enhanced remote sensing images with higher contrast and richer details without introducing saturation artifacts.

Matlab code

Download: Code.zip

Supplemental experiments

From left to right: original image, Histogram Equalization (HE), AGCWD method [1], DWT-SVD method [2] and the proposed method.

   
   
   
   
   


Reference

[1] S.-C. Huang, F.-C. Cheng, and Y.-S. Chiu, “Efficient contrast enhancement using adaptive gamma correction with weighting distribution,” IEEE Trans. Image Process., vol. 22, no. 3, pp. 1032–1041, 2013.

[2] H. Demirel, G. Anbarjafari, and C. Ozcinar, “Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition,” IEEE Geosci. Remote Sens. Lett., vol. 7, no. 2, pp. 333–337, 2010.



Extension: Ordinary Image Enhancement

Top: original images. Bottom: our results.

   
   




Results with different values in Equation (8) to separate high and low energy parts

From left to right: original image, results with 0.1, results with 0.01 and results with 0.0001.