Image fusion is an important field in many image processing and analysis tasks in which fusion image data are acquired from multiple sources. In this paper, we investigate the Image fusion of remote sensing images which are highly corrupted by salt and pepper noise. In our paper we propose an image fusion technique based Markov Random Field (MRF). MRF models are powerful tools to analyze image characteristics accurately and have been successfully applied to a large number of image processing applications like image segmentation, image restoration and enhancement, etc.,. To de-noise the corrupted image we propose a Decision based algorithm (DBA). DBA is a recent powerful algorithm to remove high-density Salt and Pepper noise using sheer sorting method is proposed. Previously many techniques have been proposed to image fusion. In this paper experimental results are shown our proposed Image fusion algorithm gives better performance than previous techniques.
Title = "An Improved Image Fusion Scheme Based on Markov",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "3",
Pages ="1 - 87",
Year = "2014",
Authors ="Nalla Aravind Babu M. Veeraswamy"}