Comparative Analysis of Image Filtering for Dental Caries Image Denoising

A pre-processing algorithm is crucial in enhancing diagnostic capabilities in various domains, including medical diagnostics. In particular, dental X-ray imaging is a primary tool for interpreting diseases and making accurate pathology decisions in clinical settings. However, the presence of noise in dental X-ray images poses a significant challenge, affecting diagnostic accuracy. This paper aims to compare and evaluate different image filtering methods for denoising dental caries images to improve diagnostic accuracy. Our This study evaluates evaluated the effectiveness of standard filters commonly used applied in medical imaging, including encompassing the Gaussian Filter, Median Filter, Average Filter, Guided Filter, Bilateral Filter, and Wiener Filterfilters. We Tsystematically compare these filters were systematically compared to identify the most suitable one for denoising dental X-ray images under various noise scenarios, including: Gaussian, Salt and Pepper, and Poisson noise. The results demonstrated that the Guided filter is was particularly effective in removing Gaussian and Poisson noise, while the Median filter is was well-suited for removing Salt and Pepper noise. Both filters outperformed others in qualitative and quantitative analyses, highlighting their efficacy in enhancing the visualization of dental structures and improving diagnostic accuracy in dental imaging.