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In order to enhance the quality governance in automotive firms the fault analysis and categorization is designed with optimal image renewals employing swarm intelligence scheme with improved precision classifier. Methodology: Preliminarily the accumulated information is preprocessed for eradicating the undesirable noise and renewal is achieved employing non – local means scheme, followed by which five characteristics like arithmetic mean, variance, standard deviation, skewness and auto correlation are mined. The mined characteristics are sent to the feed forward neural network (FFNN) classifier for recognizing faults in the computerized segments produced in the firms. In FFNN the particle swarm optimization (PSO) is employed to optimize the characteristics for effective fault identification in metal sheets. Results: The experimental analysis reveals that the designed FFNN – PSO scheme acquires improved performance with increased rate of accuracy of 92.86%, sensitivity rate of 95.24%, specificity rate of 90.48%, G – mean rate of 97.47% and precision rate of 90.90% evaluated against the prevailing classifiers.

In many applications, such as astronomy, remote sensing, medical imaging, military detection, public security, and video technology, images are the main sources of information. But, due to some reasons, observed images are degraded. The degradations are mainly caused by blur and noise. The aim of image restoration is to obtain restored image which should be as close as the original image. Wavelet transforms and neural networks have proven to be very efficient and effective in analyzing a very wide class of signals and phenomena. Wavelet expansion allows a more accurate local description and separation of signal characteristics. Here image processing is introduced for industrial applications in automatic visual inspection system because visual inspection system is not able to identify the small flaws in the industrial products.