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This article presents the detailed analysis of the local pixel grouping-principle component analysis (LPG-PCA) algorithm in denoising and deblurring of medical images. Inefficient diagnosis of the medical images containing lot of information is often affected by the noise and artifacts. In order to remove these noises and artifacts, a statistical decorrelation technique, LPG-PCA is used which is found to be one of the efficient methods, which could be used in improving the performance of medical images. For better preservation of local structures of the image, a pixel and its nearest neighbors are modeled as a vector variable, which leads to the selection of similar intensity characteristics. Denoising method used in this article is done in two stages for improving the denoising performance. The smoothening caused by the denoising process is removed by using LPG-PCA along with adaptive sparse domain representations in the deblurring process. This involves clustering of data and finding the subdictionary of each cluster using LPG-PCA. Experimental results show that an average improvement of 2.9 and 5.1 dB is found in the computed tomography and magnetic resonance imaging images using denoising and deblurring process. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 157-170, 2013 [ABSTRACT FROM AUTHOR]

Investigations were carried out on the seasonal incidence of Spodoptera litura on groundnut at the Regional Research Station, Vriddhachalam, Tamil Nadu. Seven field experiments were conducted during Kharif 2001, Kharif 2002, Kharif 2003, Kharif 2004, Rabi 2001-02, Rabi 2002-03, and Rabi 2003-04 using groundnut variety VRI 2. The presence of Spodoptera was noticed two weeks after sowing and continued to exist up till harvesting of the crop. The peak incidence of pest occurs around 50-60 days after sowing. The correlation and regression studies showed that during Kharif season, the individual weather parameters have no significant correlation with the abundance of Spodoptera, but all weather parameters together can have influence up to 90% on the incidence of Spodoptera. During Rabi season also the effect of weather parameters individually on the abundance of Spodoptera was not significant. But, the R² value indicated that the influence of weather parameters on the incidence of Spodoptera was 98%. The forecasting model developed for Kharif season was Y = 12.309 + 2.37 X23, where, Y-Percent pest Incidence, X23 = Adjusted Minimum Temperature (2-week lag). The model developed for Rabi season was Y = 0.189 + 2.039 X12, where, Y-Percent pest Incidence, X12 = Adjusted Maximum Temperature (1-week lag). These models were validated with individual season data. [ABSTRACT FROM AUTHOR]