Decision Based Median Filter using Particle Swarm Optimization for Impulsive Noise

Abstract

Abstract: Infrared image sensors and communication medium often introduce impulse noise in image acquisition and transmission. Most commonly available filters to remove impulsive noises are median filters with different versions, but the most important drawbacks identified with them are low noise suppression and edge blurring. To preserve the sharp and valuable information present in the image, the filtering algorithms should preserve the information available in them. The proposed work consists of particle swarm optimization based weight adaptation approach in the design of the filter. The filter weights are adapted and optimized directly to restore a corrupted pixel in a mean square sense. This proposed work results in replacement of noisy pixels by near originals along with its edge direction. The objective parameters used are Peak Signal to Noise Ratio, Mean Absolute Error, and Correlation. This proposed research work is designed at presenting a new filtering framework for impulse noise removal using Particle Swarm Optimization

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