**5. Discussion**

The experiments show that to create an adaptive system, you can use the technique of selecting filters, for which you need to choose a filter that provides the best symmetry of the images under appropriate conditions. The success of the experiments inspires the authors to further test all the most widely used filters in different conditions, which will allow (according to an objective assessment) choosing filters that provide the best symmetry of image display for specific conditions.

In image segmentation evaluation, the structural similarity index (SSIM) estimates the visual impact of shifts in an image [26]. The SSIM consists of three local comparison functions, namely luminance comparison, contrast comparison, and structure comparison, between two signals excluding other remaining errors. The SSIM is computed locally by moving an 8 × 8 window for each pixel, unlike the peak signal-to-noise ratio (PSNR) or root-mean-square error (RMSE), which are measured at the global level. "Even though SSIM can be applied in the case of an edge detection evaluation, in the presence of too many areas without contours, the obtained score is not efficient or useful (in order to judge the quality of edge detection with the SSIM, it is necessary to compare with an image having detected edges situated throughout the image areas)" [26]. This is why PSNR was chosen as a simple and widespread method of global assessment. Other popular evaluation methods [26] do not have strong superiority and have less popularity. Due to this, this paper had an unsettled task of studying the behavior of filters in a particular education (without strong differences).

The success of this study encourages the authors to expand the number of methods and conditions in the next study, in particular to conduct experimental studies of additional methods [27]. This paper analyzes the speed in contrast to the idea of lighting level.
