Multi-view image recognition is crucial for numerous applications such as autonomous vehicles and robotics, where accurate 3D reconstructions from 2D images are essential. However, the presence of various noise factors like motion blur, variable lighting, and changes in the field of view can significantly degrade the quality of these reconstructions. Through a series of experiments using simulated data created in Blender, I explore the effectiveness of different de-noising methods, including AI-based and traditional image processing techniques. My findings indicate that specific adjustments in the Gaussian Splatting data preprocessing can significantly mitigate the adverse effects of noise, leading to more accurate and reliable 3D reconstructions. The study contributes to the field by providing a detailed analysis of noise impact and proposing viable solutions to enhance the fidelity of multi-view image reconstructions in noisy environments. The code and resources developed for this project are available in the GS-preprocessor repository on GitHub. https://github.com/surftijmen/GS-preprocessor.