Abstract
Nowadays the presence of noises in the extracted image is a major constrain in the Image Processing environment. An ultimate goal of this paper is to optimize the watermark image using particle swarm optimization. Earlier, the research focused on the video watermarking process where the watermark image is embedded into the video as invisible and can be sent to the receiver side for an extraction. While at the extraction stage, the image what embedded was not been retrieved at the receiver side due to the presence of various noises and occurance of errors in the medium. Hence while comparing the extracted image with the original image, the accuracy was poor and non reconstructable. So this paper serves solution for the above said problem using Particle Swarm Optimization Technique. As the end, the extracted image can be reconstructed to the maximum extent as equal to the input image. This scheme can be widely used in the Medical Imaging application, Defence, etc.
Highlights
Particle Swarm Optimization (PSO) is a sturdy technique based on the drive and intelligence of swarms
This Optimization technique applies the concept of social interaction to problem solving techniques
This Optimization Technique was developed by James Kennedy and Russell Eberhart in the year 1995
Summary
Particle Swarm Optimization (PSO) is a sturdy technique based on the drive and intelligence of swarms This Optimization technique applies the concept of social interaction to problem solving techniques. PSO is considered to be an efficient optimization algorithm by searhcing an entire high – dimensional problem space. It uses number of agents considered as particles; that constitute a swarm moving around in the search space looking for the best solution. The basics of Particle Swarm Optimization include: The standard version of the PSO algorithm is essentially described by the following two simple velocity and position update equations, shown in 1 and 2 respectively. Pid → Historically best position of the ith particle in the dth dimension. R1 and R2 → n – dimensional vectors. c1 and c2 → Cognitive and Social parameters
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