<p>Intelligent transportation systems are currently highly popular for the efficient detection of vehicles and require high-quality image data. With the advancement of technology, computer vision technology plays a vital role in developing robust and efficient systems, making the process easier than traditional methods. Data acquisition serves as the initial step in these systems, wherein various sensors or devices are employed to capture data in video or image formats. The issue of interference, stemming from both internal and external factors during the data acquisition phase, renders some samples inappropriate for some instances. Consequently, it becomes necessary to decontaminate the data to facilitate higher-level processes in computer vision. Several filters have been proposed and utilized earlier for quality enhancement. This paper delves into the challenges and issues encountered during data acquisition, as well as the filters implemented to enhance performance. Different filters are also applied to evaluate their performance across various noise and data types. The effectiveness of these filters is illustrated based on metrics such as PSNR, MSE, SNR, and MD using two popular datasets, Urban Tracker and KoPER. Quantitative results demonstrate that while simple filters achieve moderate denoising, advanced methods significantly improve signal fidelity and edge preservation. On KoPER, Adaptive Median and Advanced Guided filters achieve PSNR &gt; 24 dB with processing times under 1 ms. These findings confirm that advanced, structure‑aware filters offer the optimal trade‑off between noise suppression and detail retention for real‑time ITS applications.</p>