Research Article Open Access

An Efficient Denoising Algorithm for Impulse Noise Removal

A. Rajamani1 and V. Krishnaveni1
  • 1 , India


Now-a-days the images acquired by the digital cameras and defective sensors tend to introduce noises during either image acquisition or transmission process. The quality of the image is degraded in a significant measure. Lot of research works was carried out for several decades to denoise the impulse noise and each approach has its own merits and demerits. This study deals with a new denoising approach for the gray scale images to discard fixed type salt and pepper noise present in the images. This algorithm was implemented for gray scale images such as Lena and cameraman and the performance results are really challenging both qualitative and quantitative wise. This study considered the performance metrics like PSNR and MSE for quantitative measure and presents better results for low density noise level to high density noise level (up to 100%), when compared to other existing filters. The visual interpretation shows that this method proves better in qualitative analysis by human perception too. In addition to this the proposed approach decreases the computational and hardware complexity by an appreciable manner since traditional sorting schemes does many comparisons and that were very much avoided. Thus very fast operation could be achieved. This study deals with neighborhood pixel comparison which are confined to previous pixel and the pixel next to the processing pixel under consideration, the absence of sorting saves much time and number of operations, which in turn speed of operation is increased and better reconstruction of images is achieved.

Journal of Computer Science
Volume 11 No. 1, 2015, 57-63


Submitted On: 18 March 2014 Published On: 10 September 2014

How to Cite: Rajamani, A. & Krishnaveni, V. (2015). An Efficient Denoising Algorithm for Impulse Noise Removal. Journal of Computer Science, 11(1), 57-63.

  • 1 Citations



  • Salt and Pepper Noise
  • Standard Median Filter
  • Peak Signal to Noise Ratio
  • Mean Square Error