Automated analysis of video scenes requires the separation of moving objects from the background environment, which could not separate moving items from the background in the presence of noise. This paper presents a method to solve this challenge; this method uses the Directshow framework based on the pipe-and-filter architecture. This framework trace in three ways. In the first step, the values of the MSE, SNR, and PSNR criteria calculate. In this step, the results of the error criteria are compared with applying salt and pepper and Gaussian noise to images and then applying median, Gaussian, and Directshow filters. In the second step, the processing time for each method check in case of using median, Gaussian, and Directshow filter, and it will result that the used method in the article has high performance for real-time computing. In the third step, error criteria of foreground image check in the presence or absence of the Directshow filter. In the pipe-and-filter architecture, because filters can work asynchronously; as a result, it can boost the frame rate process, and the Directshow framework based on the pipe-and-filter architecture will remove the existing noise in the video at high speed. The results show that the used method is far superior to existing methods, and the calculated values for the MSE error criteria and the processing time decrease significantly. Using the Directshow, there are high values for the SNR and PSNR criteria, which indicate high-quality image restoration. By removing noise in the images, you could also separate moving objects from the background appropriately. Manuscript profile

Noise removal operation is commonly applied as pre-processing step before subsequent image processing tasks due to the occurrence of noise during acquisition or transmission process. A common problem in imaging systems by using CMOS or CCD sensors is appearance of the salt and pepper noise. This paper presents Cellular Automata (CA) framework for noise removal of distorted image by the salt and pepper noise. In order to enhance the performance of the designed CA for noise removal, a parallel programming approach has been adopted and implemented on GPU. The results obtained show that the proposed CA models implemented on general purpose processor and GPU are able to suppress noise in high noise intensity up to 90 percents. The proposed CA implemented on GPU has successfully outperformed the method implemented on CPU by factor of 2 for gray scale image and factor of 10 for color images. Manuscript profile