1. PHGRMS: A Parallel Hypergraph Based Root Mean Square Algorithm for Image Denoising
Guide: Dr. Rajesh Kanna, Vellore Institute of Technology, India
I presented this work at the 22nd ACM Symposium on High Performance Parallel and Distributed Computing, HPDC 2013, New York, USA . It also won the BEST POSTER Award at the conference among 26 selected posters.
I worked on this project with Saurabh Jha (final year undergraduate) on designing 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. For SP noise removal, we reduce the original 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 tested P-HGRMS using standard images from the Berkeley Segmentation dataset on NVIDIAs Compute Unified Device Architecture (CUDA) for noise identification and attenuation. P-HGRMS maintains the noise removal efficiency and outperforms its sequential counterpart by 6 to 18 times (6x - 18x) in computational efficiency. This work won the Best Poster award at HPDC 2013 in New York . The extended abstract and slides from my talk are here in the Research section.
The basic working of the algorithm can be explained by the figure below.
1. P-HGRMS maintains the noise removal efficiency of HGRMS algorithm as evident from PSNR values.
2. It outperforms HGRMS by 6 to 18 times (6x - 18x) in computational efficiency.
 K. Kannan, B. Rajesh Kanna, and C. Aravindan (2010), Root mean square filter for noisy images based on hypergraph model, Image and Vision Computing, 28 (9), 1329-1338, Elsevier, DOI: 10.1016/j.imavis.2010.01.013, 5- Year impact factor- 1.84
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