Development of an Improved Edge Detection Algorithm for Noisy Coloured Images using Particle Swarm Optimization

Development of an Improved Edge Detection Algorithm for Noisy Coloured Images using Particle Swarm Optimization.

Abstract

This research work presents an improved algorithm using particle swarm optimization based on vector order statistics.

The proposed algorithm was implemented using MatLab 2013 script.

The algorithm addressed the performance of edge detection in images, with a view to minimizing broken, false and thick edges whilst reducing the presence of noise as well as computational time.

A collection scheme based on step and ramp edges was developed for the edge detection algorithm, which explores a larger area in the images in order to reduce false and broken edges.

The efficiency of this algorithm was tested on two Berkeley benchmark images in clean and noisy environments with a view to comparing results, both visually and quantitatively, with those obtained using proven edge detection algorithms such as the Sobel, Prewitt, Roberts, laplacian, and canny edge detection algorithms.

The algorithm was also applied to facial and remotely sensed images with a view to testing the algorithm on real-life images.

The pratt figure of merit (pfom) was used as a quantitative comparison between the developed algorithm and the proven edge detection algorithms.

The benchmark value for the pfom is between 0-1, which shows efficient detection of edges as the value tends towards 1.

The quantitative results obtained using pfom on the test images in a clean environment for the Sobel, Prewitt, Roberts, laplacian, canny, and the proposed edge detection algorithms are 0.4209, 0.4195, 0.4181, 0.7048, 0.8421, and 0.8480, respectively.

This showed that the proposed algorithm detected more edges in a clean environment as the value obtained is nearest to 1.

The pfom on the test images in a noisy environment for the Sobel, Prewitt, Roberts, laplacian, canny, and the proposed edge detection algorithms are 0.4191, 0.4191, 0.2807, 0.2811, 0.5606, and 0.8458 respectively.

This showed that the proposed algorithm detected more edges in a noisy environment as the value obtained is nearest to 1.

The proposed algorithm achieved a peak signal-to-noise ratio (psnr) of 57.7320db in an environment containing ≤ 33% of the noise level.

This result signifies 3% improvement in the detection of edges in a noisy environments as compared with the proven traditional edge detection algorithms which achieved an average psnr of 22-35db.

Introduction

Background

Edge detection can be defined as the process of identifying set of connected pixels that forms a boundary between two disjoint regions (gang et al., 2008).

It can also be defined as the process of locating and identifying sharp discontinuities in images (rashmi et al., 2013).

It is mostly used in image analysis to preserve image features and partition images into regions of interest. The discontinuities in these images can be caused by (ghasemi et al., 2011):

• Discontinuity in-depth and/or surface color and
• Reflection of light, shadows, and illumination.

Edge detection is an image segmentation technique in which images are partitioned into meaningful regions of interest.

Some of the practical applications of edge detection algorithms are in the face and fingerprint recognition, location of objects in satellite images, medical images, and computer-aided surgery or diagnosis amongst others (rashmi et al., 2013).

One of the most important challenges of the edge detection algorithm is to detect the edges in noisy images.

Many traditional edge detection algorithms have been developed to overcome noise such as Sobel, Prewitt, Roberts, and gradient-based edge detection algorithms, etc. (rashmi et al., 2013).

These traditional edge detection algorithms are very fast but they cannot perform well on noisy images. Hence, the significant problem of these edge detection algorithms is displacement, removed edges, false and broken edges(maini & aggarwal, 2011).

Noise phenomenon is an obstacle in the detection of continuous edges as it causes some variation of pixel intensities, thus reducing the performance of an edge detection algorithm in noisy images (setayesh et al., 2013).

It also leads to unclear and displaced edges (Chaudhary & Gulati, 2013). Many edge detection Algorithms have been developed in the literature over the past years to improve the precision of recognized edges.

However, they still suffer from producing broken edges and false edges due to the noise effects (maini & aggarwal, 2011).

Therefore, an improved edge detection algorithm is required to detect edges with greater continuity in noisy images in order to reduce the shortcomings of traditional edge detection algorithms.

References

Abdul a., & funjan w. (2013). Denoising an image based on particle swarm optimization (pso) algorithm. Journal of babylon university/pure and applied sciences, 21(5), pp.1511-1518.
Akram a., & ismail a. (2013). Comparison of edge detectors. International journal of computer science and information technology research (ijcsitr), 1(1), pp.16-24.
Almadhoun m. D. (2013). Improving and measuring color edge detection algorithm in rgb color space. International journal of digital information and wireless communications, 3(1), pp.19-24.
Anila, & devarajan. (2010). Simple and fast face detection system based on edges.International journal of universal computer sciences, 1(2), pp.54-58.
Ashour a. S., el-sayed m. A., waheed s. E., & abdel-khalek s. (2014). New method based on multi-threshold of edges detection in digital images. International journal of advanced computer science & applications, 5(2), pp.90-99.
Bhardwaj k., & mann p. S. (2013). Edge detection in digital images corrupted by salt and pepper noise using adaptive neurofuzzy inference system(anfis). International journal of computer applications, 72(12), pp.36-41.