IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 4, Ver. VI (Jul – Aug. 2014), PP 65-70 www.iosrjournals.org www.iosrjournals.org 65 | Page Comparative Performance Analysis of SALT and PEPPER Noise Removal Sakshi Tiwari 1 , Prof. Akhilesh Bansiya 2 , Prof. Raj Kumar Paul 3 1 (Research scholar,M.tech in Computer science,VIT Bhopal /RGPV,India) 2 (Deppt. Of Computer science VIT Bhopal/RGPV,India) 3 (HOD,deppt. Of Computer science VIT Bhopal/RGPV,India) Abstract: Noise is an important factor which when get added to an image reduces its quality and appearance. So in order to enhance the image qualities, it has to be removed with preserving the textural information and structural features of image. There are different types of noises exist who corrupt the images. Selection of the de-noising algorithm is application dependent. Noise removal from a contaminated image signal is a prominent field of research and many researchers have suggested a large number of algorithms and compared their results. The main thrust on all such algorithms is to remove impulsive noise while preserving image details. These schemes differ in their basic methodologies applied to suppress noise. Some schemes utilize detection of impulsive noise followed by filtering whereas others filter all the pixels irrespective of corruption. In this section an attempt has been made for a detail literature review on the reported articles and studies their performances through computer simulation. We have classified the schemes based on the characteristics of the filtering schemes and described are below. At the end of paper, a comparative study of all these algorithms in context of performance evaluation is done and concluded with several promising directions for future research work. Keywords: Noise, Textural information, Image de-noising algorithm.. I. Introduction A very large portion of digital image processing is deployed in image restoration. Image restoration is the removal or reduction of degradations which occurred while the image is being obtained [1]. Image processing is an important area in the information industry. A crucial research is how to filter noise caused by the nature, system and processing of transfers and so on. Image de-noising has been one of the most important and widely studied problems in image processing and computer vision. The need to have a very good image quality is increasingly required with the advent of the new technologies in a various areas such as multimedia, medical image analysis, aerospace, video systems and others. Indeed, the acquired image is often marred by noise which may have a multiple origins such as: thermal fluctuations; quantify effects and properties of communication channels [2].A noise is introduced in the transmission medium due to a noisy channel, errors during the measurement process and during quantization of the data for digital storage. Each element in the imaging chain such as lenses, film, digitizer, etc. contributes to the degradation .Image de-noising is often used in the field of Figure – 1 Image De-noising Photo-graphy or publishing where an image was somehow degraded but needs to be improved before it can be printed. Image de-noising finds applications in fields such as astronomy where there solution limitations are severe, in medical imaging where the physical requirements for high quality imaging are needed for analyzing images of unique events, and in forensic science where potentially useful photographic evidence is sometimes of extremely bad quality [2]. A two-dimensional digital image can be represented as a 2-dimensional array of data s(x,y), where