Algorithm for improving the quality of mixed noisy images

Abstract

Digital images are one of the effective means of receiving, transmitting, exchanging and expressing information. However, the digital image is not always of the expected quality, that is, as a result of the influence of various factors, noises appear in the image. This significantly reduces the accuracy and quality of the data in the image. Segmentation and recognition of objects from a low-quality image creates many additional problems. For example, it requires image quality enhancement based on powerful pre-processing algorithms. One such problem is to eliminate multiple noises in the image, and it is one of the pressing issues of digital image processing. This paper is devoted to solving the problem of image reconstruction by eliminating the mixed condition of Gaussian, salt-pepper and Poisson noises, which are common in digital images, in which one filter that optimally reduces each type of noise is selected and all combinations of them are applied to the mixed noise image to maximize the images. The idea of restoration has been put forward. The quality of the reconstructed images was evaluated by the NIQE criterion, which is one of the no-reference quality indicators, and a rule for automating the image processing process was proposed based on its values.