Retinex theory for color image enhancement: A systematic review

A short but comprehensive review of Retinex has been presented in this paper. Retinex theory aims to explain human color perception. In addition, its derivation on modifying the reflectance components has introduced effective approaches for images contrast enhancement. In this review, the classical theory of Retinex has been covered. Moreover, advance and improved techniques of Retinex, proposed in the literature, have been addressed. Strength and weakness aspects of each technique are discussed and compared. An optimum parameter is needed to be determined to define the image degradation level. Such parameter determination would help in quantifying the amount of adjustment in the Retinex theory. Thus, a robust framework to modify the reflectance component of the Retinex theory can be developed to enhance the overall quality of color images

AODV Protocol Improvement using Intelligent Clustering

The paper proposes an algorithm for intelligent cluster head (CH) election in clustered-AODV-based routing. Initially, the K-mean for clustering the MANET nodes have been used according to their distances between them. Next, the intelligent CH election using soft computing techniques that includes sequential hybrid Fuzzy-Genetic controller for this decision making have also been applied. The paper simulation shows that the clustered–AODV-based routing protocol can be modified by changing the ordinary known AODV protocol (Classical AODV) to comply with the clustered network