Adaptive algorithm for reducing pulse noise level in images from CCTV cameras

An optical signal is usually converted into an electrical one by using photosensitive matrices with a large number of discrete elements based on charge-coupled device (CCD) technology or CMOS technology. One of the disadvantages of CCD and CMOS technologies is the impulse conversion noise that appears on digitized images, impairing visual perception and significantly reducing the likelihood of correct identification in pattern recognition tasks. Traditionally, impulse noise is removed from images using median filters with a fixed aperture within each iteration of full-format processing. However, such filters reduce the sharpness of the reconstructed image at high noise levels or insufficiently suppress the interference under the same noise conditions. These setbacks call for a need to develop an adaptive median filtering algorithm, which would produce a reconstructed image as a joint result of processing with median filters with different apertures. The essence of this algorithm is to select image areas with different noise levels and process these areas with filters with different apertures. As an objective criterion for assessing the efficiency of the proposed filtering algorithm, the authors used the criterion of the maximum correlation coefficient between noise-free and non-noisy images at various values of the noise variance. The mathematical modeling performed in this study allowed finding that with an increase in the impulse noise variance, the gain of the adaptive median filtering algorithm increases exponentially, in comparison with the algorithms using the filters with a fixed aperture value. The proposed algorithm can be used for pre-preprocessing images intended for recognition by machine vision systems, scanning text, and improving subjective image characteristics, such as sharpness and contrast.