  This paper presents a parallel Salt and Pepper (SP) noise removal algorithm
in a grey level digital image based on the Hypergraph Based Root Mean Square
(HGRMS) approach. HGRMS is generic algorithm for identifying noisy pixels in
any digital image using a two level hierarchical serial approach. However, for
SP noise removal, we reduce this algorithm to a parallel model by introducing a
cardinality matrix and an iteration factor, k, which helps us reduce the
dependencies in the existing approach. We also observe that the performance of
the serial implementation is better on smaller images, but once the threshold
is achieved in terms of image resolution, its computational complexity
increases drastically. We test P-HGRMS using standard images from the Berkeley
Segmentation dataset on NVIDIAs Compute Unified Device Architecture (CUDA) for
noise identification and attenuation. We also compare the noise removal
efficiency of the proposed algorithm using Peak Signal to Noise Ratio (PSNR) to
the existing approach. P-HGRMS maintains the noise removal efficiency and
outperforms its sequential counterpart by 6 to 18 times (6x - 18x) in
computational efficiency.
