Multiresolution Edge Detection using Particle Swarm Optimization

Year 2017, Volume: 1 Issue: 1, 11 - 17, 31.03.2017

ALAA Eleyan , Muhammad Anwar

Abstract

In this paper, a
heuristic approach based on biologically inspired Particle Swarm Optimization
(PSO) is proposed to be used at multiresolution level to improve the quality of
detected edges.  In the preprocessing
stage, one level of Discrete Wavelet Transform (DWT) or Dual-tree Discrete
Wavelet Transform (DT-DWT) is applied to the input image to create new subbands
images. PSO is then applied to each one of these subband images. The output
image containing the detected edges is obtained by reconstructing it from the
processed subband images using the inverse transform. An objective function is
proposed for the PSO to evaluate edges during the heuristic search within the
image space. Also, automatic thresholding is introduced which is used to
automatically threshold the output of the PSO into binary image. Performance the
proposed approach is evaluated and compared with other well-known edge detectors
such as Sobel and Canny using Kodak image database. The results from objective
evaluation using Peak Signal-to-Noise-Ratio (PSNR) and Root Mean Square Error
(RMSE) showed that the proposed approach has a better and/or comparable
performance compared to other edge detectors. 

Keywords

Edge Detection, Particle Swarm Optimization, Discrete Wavelet Transform, Dual Tree Complex Wavelet Transfor

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