Operating semiconductor quantum dots as quantum bits requires isolating single electrons by adjusting gate voltages. The transitions of electrons to and from the dots appear as a honeycomb-like pattern in recorded charge stability diagrams (CSDs). Thus, detecting the pattern is essential to tune a double dot, but manual tuning is seriously time-consuming. However, automation of this process is difficult because the transitions’ contrast is often low, and noise and background disorder potential shifts disturb the CSDs. Therefore, the signal-to-noise ratio needs to be increased to improve the detection of the line pattern. For this purpose, we evaluate a representative set of edge-preserving smoothing filters and compare them both quantitatively and qualitatively by suitable metrics and visual assessment. We generate artificial data to use full-reference metrics for the evaluation procedure and to optimize the filter parameters. Based on the results of this article, the methods attain a moderate to good amount of noise reduction and structure improvement dependent on the different CSD qualities. In conclusion, we suggest introducing the block-matching and three-dimensional transform-domain filter into the automated tuning processing pipeline. If the data are corrupted by significant amounts of random telegraph noise, the bilateral filter and the rolling guidance filter are also good choices.