Color constancy is an element of human

vision framework which guarantees that the apparent

color of items under fluctuating light conditions generally

remains constant. It is fundamentally used to eliminate

the color cast in the picture. Color Cat is a quick and

precise learning-based methodology for accomplishing

computational color constancy. However, despite

everything it confronts a few limitations like poor

brightness due to normalization used. Furthermore it

doesn't promise edge preservation. So to overcome these

issues a CEA strategy has been proposed which is a

hybrid model based on Color Cat, Edge preservation

filter and Adaptive histogram Equalization. As Adaptive

histogram Equalization is exceptionally valuable for

contrast improvement and edges are protected by edge

preservation filter. Experimental results show that the

proposed CEA approach outperforms over existing

technique