Digital image noise removal towards soybean and cotton plant disease using image processing filters

Abstract

The processing of noisy images does not provide accurate outcomes. In most existing techniques, many filters have been employed to eradicate the noises and enhance the image quality. However, most of the works fail to generate enhanced outcomes because of degraded capability and increased time consumption. Hence, in the proposed research work, a novel pre-processing method called the optimization assisted cascaded filtering approach (OptCFA) is introduced. In the proposed Opt_CFA model, the Gaussian Amended Bilateral filter (GABF) is designed where the multilevel thresholds are calculated for the GABF outcomes. The filter parameters are optimized using the Amended Pelican optimization algorithm (AmPel) for first stage noise removal. The outcomes are then cascaded with an Extended Savitzky-Golay filter (E_SGF) for second stage noise removal. The proposed Opt_CFA model is a single approach with the integration of GABF, AmPel and E_SGF methods. In the results section, some existing pre-processing filters, like the median, bilateral, and Gaussian filters, are analyzed for comparison. The individual performance of GABF and E_SGF methods used in the proposed model are also analyzed. Through the proposed technique, the noises can be extensively removed, and the image quality can be greatly enhanced. Several existing filters are compared with the proposed model, whereas the results are analyzed using PYTHON.