Electroencephalographic (EEG) recordings are often contaminated with several artifacts.Powerline interference and baseline noise is always present in EEG response of every patient. A number of strategies are available to deal with noise effectively both at the time of EEG recording as well as during preprocessing of recorded data. The aim of the paper is to give an overview of the most common sources of noise and review methods for prevention and removal of noise in EEG recording ,including elimination of noise sources.

Nowadays, Image enhancement finds enormous image processing applications, which are related to practical situations, Contrast enhancement is one among the different image enhancement techniques that intends to improve the image visibility. Though several works for local contrast enhancement are available in the literature, the effectiveness remains an issue and the enhancement performance needs to be improved. In this paper, a local contrast enhancement technique is proposed for both gray scale images and RGB color images. The proposed technique is comprised of two stages of enhancement, namely, local sta tistics based image enhancement and Genetic Algorithm based local contrast enhancement. The former stage is a pre enhancement stage and the later is the major stage of enhancement. In the former stage, the image is processed in window basis and the local statistics of the image is obtained. Based on the local sta tistics, the image is enhanced. In the later stage, the window based operation is performed over the pre enhanced image and the local contrast is enhanced. The Genetic Algorithm aids in searching of an optimal contrast factor, which plays vital role in the contrast enhancement. The technique is evaluated with both gray scale images as well as RGB color images and performance is compared with the existing contrast enhancement techniques.

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