In this paper, a new approach for image edge detection using wavelet based ant colony optimization (ACO) is proposed. The proposed approach applies discrete wavelet transform (DWT) on the image. ACO is applied to the generated four subbands (Approximation, horizontal, vertical, and diagonal) separately for edge detection. After obtaining edges from the 4 subbands, inverse DWT is applied to fuse the results into one image with same size as the original one. The proposed approach outperforms the conventional ACO approach.

Ant Colony Optimization (ACO) is used to obtain the edges of an image which is acquired from sampling and quantization of a continuous image. Such techniques generate a pheromone matrix that represents the edge information at each pixel position on the routes formed by ants dispatched on the image. However, when the image is buried in noise, ACO performance deteriorates. In this paper, we propose to use discrete wavelet transform (OWT) as a preprocessing step with ACO to enhance image edge detection. The proposed algorithm creates a pheromone matrix to stand for the edges of the low frequency component obtained from the OWT decompositions, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of ants are driven by the local variation of the image's intensity values. The proposed approach shows a significant performance and capability of detecting edges superior to existing techniques.