Road surface quality assessment is one of the most popular tasks worldwide. To solve it, there are many systems, mainly interacting with images of the roadway. They work on the basis of both traditional methods (without using machine learning) and machine learning algorithms. To increase the effectiveness of such systems, there are a sufficient number of ways, including improving image quality. However, each of the approaches has certain characteristics. For example, some of them produce an improved version of the original photo faster. The analyzed methods for improving image quality are: noise reduction, histogram equalization, sharpening and smoothing. The main indicator of effectiveness in this study is the average time to obtain an improved image. The source material is 10 different photos of the road surface in 5 sizes (447x447, 632x632, 775x775, 894x894, 1000x1000) in png, jpg, bmp formats. The best performance indicator according to the methodology proposed in the study was demonstrated by the "Histogram equalization" approach, the "Sharpening" method has a comparable result.