Image denoising with bank filters using the maximum likelihood estimation

Recently, systems of intelligent image processing have been intensively developing. When solving problems of high complexity, modern methods of technical vision are required to increase the efficiency of the digital image processing process with the variability of the working scene, heterogeneity of objects, and interference. One of the trends in the development of modern information technologies is the development of highly efficient methods and algorithms for analyzing signals and images with background noises. When constructing highly-effective techniques and algorithms for image denoising, an a priori knowledge of the characteristics of distorting interference is required. In practice, in most cases, such information is missing. In this paper, we develop a new image denoising method with bank filters using the maximum likelihood estimation. We propose a new approach to using a set of heterogeneous digital image filters, such as a median filter, a Gabor filter, a non-local average filter, a spline filter, a wavelet filter, and others. The feasibility of this approach is determined by the fact that, as a rule, when considering the filtering process, a Gaussian character of the noise distribution density is assumed. Moreover, the effectiveness of various filtering methods on real images recorded against the background of noise will be different. This is due to the fact that under real observation conditions, the noise distribution density may differ from the Gaussian one. This explains the difference in the qualitative filtering characteristics of the same image by different filters. Experimental studies have shown the operability and high efficiency of the developed method, which allows improving the quality of image filtering.