Hill Climbing-based Histogram Equalization for Camouflage Object Detection

Camouflage aims at making objects disappearing in the background environment by presenting similar textures, color information and patterns with the background. The camouflage objects can be divided into two groups: dark camouflage and light camouflage. To locate the camouflage objects, many existing detection algorithms have been published. And, their performance is highly related to the image enhancement as their pre-processes. Even though existing histogram equalization-based image enhancement algorithms perform well at either dark camouflage image or light camouflage image, there is still a challenge to deal with an image containing both dark camouflage and light camouflage. To meet this challenge, a new hill climbing-based histogram equalization algorithm is proposed to follow a three-step framework of segmentation, enhancement and integration. Different from existing approaches, this proposed method aims at segmenting the dark camouflage content and light camouflage content by utilizing the hill climbing algorithm. The segmented camouflage contents are enhanced by their corresponding histogram equalization. Finally, the enhanced segments are combined by an integration process to get the final output images with a satisfied quality. This hill climbingbased histogram equalization can enhance the detailed structural information in both dark and light regions of images simultaneously. Experimental and comparison results demonstrate its superior performance.