An Improved Nonlinear Decision Based Algorithm for Removal of Blotches and Impulses in Grayscale Images

A nonlinear decision based algorithm for the removal of blotches in the presence of impulse noise in grayscale images is proposed in this paper. The algorithm is implemented in two stages. In the first stage, decision rule based on the switching threshold is applied to the whole image unconditionally to detect the pixels as corrupted/uncorrupted. In the second stage the new pixel value is estimated only for the corrupted pixels. The algorithm uses an adaptive length window whose maximum size is 5×5 to avoid blurring due to large window sizes. However, the restricted window size renders median operation less effective whenever noise is excessive in which case the proposed algorithm automatically switches to mean filtering. The proposed algorithm is tested on different images. The performance of the algorithm is analyzed quantitatively in terms of Mean Square Error [MSE], Peak-Signal to-Noise Ratio [PSNR], Image Enhancement Factor [IEF] and computation time and compared with other algorithms. Extensive simulations show that proposed algorithm removes the noise effectively even at noise level as high as 50% and preserves the edges without any loss, thus producing better results in terms of the qualitative and quantitative measures of the image.