X-ray Enhancement using Homomorphic Filtering, CLAHE, and Edge Detection

Dental X-ray radiography holds tremendous importance for the diagnosis of oral health disease, but reduced contrast, noise, and corrupted edge detection commonly influence analysis as a result. This paper provides a hybrid image enhancement method through Homomorphic Filtering, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Sobel Edge Detection for increasing dental X-ray clarity and feature extraction. Homomorphic Filter enhances uniform illumination, CLAHE improves contrast with detail preservation, and Sobel Edge Detection detects major anatomical structures for improved diagnostic interpretation. A bilateral filter is also applied to remove noise without edge blurring. The technique is tested using a dental X-ray image database and compared to conventional enhancement techniques. Experimental results confirm that the designed approach significantly improves edge perception, texture resolution, and noise filtering, leading to sharper anatomical structures required for accurate diagnosis. Quantitative measurements show a major improvement in PSNR and SSIM values, confirming improved image quality with maintained structural integrity. The approach is thus highly adapted to dental diagnosis and other AI-based medical tasks.