Image denoising algorithm based on PSO optimizing structuring element

Abstract

A new image denoising algorithm is proposed to deal with information loss in the conventional morphological image denoising process. The algorithm uses median operation to improve morphological operations' performance, which called median closing operation. It gives a mathematical model of the structuring element unit (SEU) composed of a zero square matrix. The particle swarm optimization (PSO) algorithm is employed for choosing the size of structuring element. The value of peak signal to noise ratio (PSNR) is taken as a fitness function, and the transformed value of the particle's position is taken as the size of the structuring element. Experimental results show that the denoising performance of the proposed algorithm has obvious superiority than conventional morphological algorithm. It can overcome the inherent deficiency of conventional morphological operations, adaptively obtain the size of the structuring element, and effectively remove impulse noise from images, especially for the image whose signal to noise ratio value is relatively low. So it has a good prospect in image processing.