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

Abstract

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 uneven 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 nonnegative 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. INDEX TERMS Histogram equalisation; saturation artifacts; uneven expansion of intensities; fuzzification.

On One- and Two-Dimensional α–Stancu–Schurer–Kantorovich Operators and Their Approximation Properties

“…To verify the convergence of the operator defined in (13), we take the polynomial f (y 1 , y 2 ) = y 1 3 y 2 2 + y 1 2 y 2 3 and the following values of the parameters:…”

“…It plays an important role in applied and pure mathematics viz. fixed point theory [1], numerical analysis [2], image processing [3], neural networks, machine learning [4], finding solutions for ordinary and partial differential equations [5], bio-inspired soft computing [6], and robotics [7]. In the computational aspects of mathematics and the shape of geometric objects, CAGD (computer-aided geometric design) plays an interesting role in the mathematical description.…”