A Parallel model for Noise reduction of images using smoothing filters and Image averaging

Image Enhancement is the art of examining images for identifying objects and judging their significance. The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to provide `better' input for other automated image processing techniques. Smoothing is often used to reduce noise within an image or to produce a less pixilated image. This paper proposes a new idea for enhancement of an image using smoothing filters and the image noise is mostly unwanted and manifested in the pixels of an image and the main application of image averaging is noise removal. In this paper, we deal with image enhancement using smoothing filters with the help of parallel model in order to improve the quality of the image and as well as to help to solve various complex image processing tasks in the future. This paper focuses on an approach which tries to combine the advantages of the various smoothing filters techniques and proposes a parallel model. This parallel model can be implemented using various models, considering available resources with the server. After the review of earlier publications on this topic available and a comparative study of their advantages and disadvantages a parallel model that is simpler to implement and efficient has been proposed in this work.