Open Access Journal

ISSN : 2394-6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

Development of Algorithms for Contrast Enhancement of Remote Sensing Images

Author : Ramkumar.M.U 1 Kavitha N Nair 2

Date of Publication :7th January 2015

Abstract: For many years remote sensing images have played an important role in almost all fields such as meteorology, agriculture, geology, education etc. As the rising demand for high quality remote sensing images, contrast enhancement techniques are required for better visual perception and color reproduction. In this paper we explained some new enhancement techniques which use dominant brightness level analysis and adaptive intensity transformation with discrete wavelet transform and dual tree complex wavelet transform, DTCWT with principal component analysis (PCA), and a mathematical method for knee correction. Although various histogram equalization methods are proposed in the literature. They tend to degrade the overall image quality by exhibiting saturation artifacts in both low- and high- intensity layers. The proposed algorithms overcome this problem. In one method the DWT is performed first and then decompose the LL subband into low- middle-high- intensity layers using log-average luminance. Intensity layer transfer functions are adaptively estimated by using knee transfer function and the gamma adjustment function based on dominant brightness level on each layer .After the intensity transformation the enhance image is get back by taking Inverse DWT. We can do the decomposition using DTCWT in second method for better result. The contrast enhancement is performed with PCA and alternate knee correction method along with DWT and DTCWT for better results. The performance of every method is evaluated with parameters such Mean Square Error (MSE), Measure of Enhancement (EME), Peak Signal to Noise Ratio (PSNR) and Mean Absolute Error (MAE).

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DOI : 10.36647/ijerece/0000