Name
Abstract | ||
|---|---|---|
Microscopic images are often corrupted by noise, where Poisson noise is one of the major types that can damage them. The local polynomial approximation (LPA) filter supported by the intersection confidence interval (ICI) rule was considered as an efficient filter for image de-noising. However, this filter depends on several parameters that affect its performance. In order to determine the optimal parameters, the present study employed the classic (C-LPA-ICI) filter supported by optimization algorithms, namely the genetic algorithm (GA) and the particle swarm optimization (PSO) in the context of light microscopy imaging systems. Nevertheless, inclusion of the optimization algorithms increased the computational time. A novel automatic technique entitled Standard Optimized LPA-ICI (SO-LPA-ICI) is proposed. In this context, the average of the optimized ICI parameters was calculated, which obtained from both LPA-ICI-based GA (G-LPA-ICI) and LPA-ICI-based PSO (P-LPA-ICI). Thus, the proposed SO-LPA-ICI is included the optimal ICI parameters without optimization iterations. This procedure is proposed to speed up the optimized filter. A pool of 50 ratsu0027 renal microscopic images is involved to test the proposed approach. A comparative study was conducted to compare the effectiveness of the four methods, namely C-LPA-ICI, G-LPA-ICI, P-LPA-ICI, and the SO-LPA-ICI for de-noising in the presence of Poisson noise. The experimental results established the outstanding performance of the SO-LPA-ICI in terms of the PSNR, MAE, and MSSIM with 28.27, 7.65, and 0.93 values, respectively. In addition, the proposed approach achieved fast de-noising compared to the G-LPA-ICI and the P-LPA-ICI. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00521-016-2678-9 | Neural Computing and Applications |
Keywords | Field | DocType |
Image de-noising, Microscopic imaging, Poisson noise, Local polynomial approximation filter, Genetic algorithm, Particle swarm optimization | Particle swarm optimization,Mathematical optimization,Polynomial,Artificial intelligence,Adaptive filter,Microscopy,Confidence interval,Shot noise,Mathematics,Machine learning,Genetic algorithm,Speedup | Journal |
Volume | Issue | ISSN |
29 | 12 | 1433-3058 |
Citations | PageRank | References |
2 | 0.38 | 19 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amira S. Ashour | 1 | 203 | 27.96 |
| Samsad Beagum | 2 | 2 | 0.38 |
| Nilanjan Dey | 3 | 521 | 78.41 |
| Ahmed S. Ashour | 4 | 7 | 0.87 |
| dimitra pistola | 5 | 19 | 1.98 |
| Nhu Gia Nguyen | 6 | 25 | 3.45 |
| Dac-Nhuong Le | 7 | 12 | 5.01 |
| Fuqian Shi | 8 | 83 | 11.70 |