A new Cascading Algorithm for denoising images corrupted by high density noise
K. V. Ravi Teja, Pawan Kumar, N. Shanmukha Rao, P. Surya Prasad · 2016
A new Cascading Algorithm for de-noising images corrupted by Salt and Pepper noise at even high density level has been proposed. The first stage employs a Decision Based Median Filter (DBMF) which is mainly for eliminating the noise from the image, the pixels affected by noise are replaced with the median value of the processing window. The second stage employs, a Decision based Unsymmetric Partial Trimmed Variant Filter (DBUPTVF). It inspects the output of the first stage and eliminates the un-removed noisy pixels in the image based on the number of pixels in the processing window which are noisy, by Partial Trimming. The second stage is mainly used for removing any un-removed noise from the first stage and to enhance the image quality. The proposed algorithm is developed, such that noise of any density level could be removed from the image. The proposed cascading structure exhibits improved noise elimination capabilities than the existing standard filters and other cascading algorithms for up to noise densities as high as 90%. The Proposed de noising Cascading algorithm has been examined for different grayscale images. The Proposed Cascading Algorithm for de-noising, produces lesser Mean Square Error (MSE), better Peak Signal to Noise Ratio (PSNR), improved Image Enhancement Factor (IEF) and higher Structural Similarity Index Metric (SSIM) than several existing algorithms.