P-HGRMS: A Parallel Hypergraph Based Root Mean Square Algorithm for Image Denoising

TL;DR

This paper introduces P-HGRMS, a parallel hypergraph-based algorithm for efficient salt-and-pepper noise removal in images, significantly improving computational speed while maintaining noise reduction quality.

Contribution

The paper develops a parallel version of the HGRMS algorithm using CUDA, reducing dependencies and enhancing efficiency for large images in noise removal tasks.

Findings

P-HGRMS outperforms sequential HGRMS by 6 to 18 times in speed.

The algorithm maintains high noise removal efficiency comparable to existing methods.

Performance improves with larger images, demonstrating scalability.

Abstract

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.