31-03-2017, 10:36 AM
A new image elimination algorithm is proposed to treat the loss of information in the process of elimination of conventional morphological images. The algorithm uses the median operation to improve the operation of morphological operations, which it called the median closure operation. It provides a mathematical model of the structuring element unit (SEU) composed of a zero square matrix. The particle swarm optimization algorithm (PSO) is used to choose the size of the structuring element. The value of the peak signal-to-noise ratio (PSNR) is taken as a fitness function, and the transformed value of the particle position is taken as the size of the structuring element. The experimental results show that the elimination performance of the proposed algorithm has obvious superiority than the conventional morphological algorithm. It can overcome the inherent deficiency of conventional morphological operations, adaptively obtain the size of the structuring element, and effectively eliminate impulse noise from the images, especially for the image whose signal-to-noise ratio value is relatively low. So you have a good perspective on image processing.
The noisy image is grouped into subsets of pixels with respect to their intensity values and spatial distances. Using a novel fitness function, image pixels are sorted using the Particle Swarm Optimization (PSO) technique. The distance function measured the similarity / dissimilarity between pixels using not only the intensity values, but also the pixel positions. The detection technique forced grouping based on PSO, which is very simple and robust. The filtering operator restores only the noisy pixels that keep intact noise-free pixels. Four types of noise models are used to train digital images and these noisy images are restored using the proposed algorithm. The results demonstrated the efficacy of the proposed technique. Several reference images are used to produce restore results in terms of PSNR (dB) along with other parametric values. There are also some visual effects that form better restoration of digital images through the proposed technique.
The noisy image is grouped into subsets of pixels with respect to their intensity values and spatial distances. Using a novel fitness function, image pixels are sorted using the Particle Swarm Optimization (PSO) technique. The distance function measured the similarity / dissimilarity between pixels using not only the intensity values, but also the pixel positions. The detection technique forced grouping based on PSO, which is very simple and robust. The filtering operator restores only the noisy pixels that keep intact noise-free pixels. Four types of noise models are used to train digital images and these noisy images are restored using the proposed algorithm. The results demonstrated the efficacy of the proposed technique. Several reference images are used to produce restore results in terms of PSNR (dB) along with other parametric values. There are also some visual effects that form better restoration of digital images through the proposed technique.
