Image enhancement plays a vital role in dental diagnostics by enhancing the quality of radiographic photographs affected by various types of noise and low contrast. This study introduces an improved version of the Wiener filter, designed to better reduce noise while keeping essential details in dental radiographs. To test its performance, the improved Wiener filter is compared with other common filters such as Median, Gaussian, Bilateral, and the standard Wiener filter. These techniques are applied to a dental image dataset, and their results are evaluated using widely accepted image quality measures like Peak Signal-to-Noise Ratio and Structural Similarity Index. The observations show that the improved Wiener filter performs better overall by effectively cleaning the image and keeping important dental features clear with PSNR and SSIM values. This makes it a promising tool for use in both clinical diagnosis and automated dental systems. By focusing on this enhanced method, the study highlights its potential to improve the reliability of dental image interpretation in real-world conditions.