An improvement of meta heuristic techniques for noise identification and denoising using adaptive block selection based filtering

Abstract

Among various fields in the multimedia age Engineers and scientists are focusing their research in image processing The image obtained from the result need to be processed in a successful fashion by reducing the noise in the image and to make it legible Especially in forensic astrophysics space sciences transportation biometric and medical imaging application image process plays a vital role in many aspects like exchanging their information retrieval and the context One of the major challenges for the researchers is to reduce the noise present in the image and to reconstruct it by eliminating the extra noise present in the image which is known as image denoising Besides many noise removal techniques particle Swarm Optimization techniques Bacterial Foraging algorithm and Memetic algorithm techniques are discussed and expropriate in this work very detailed In this present investigation the proposed approach has been divided into two phases First phase is by using the proposed optimization techniques to identify the noise that is present in the images are studied in the first stage And the second phase is the next stage that deals with the suitable elimination techniques for the removal of the noise in the image In this approach the best results are called as the particles which are flying through by means of the space in the problem followed by the current optimum particles In this stage the each of the individual particles that are kept in the track of its coordinates space in relative problematic space approach which are connected with the best optimum solution which are known as the fitness function and it has been attained for the better result and it is called as the best values newline