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 techniques.