A Review on Advancements in Contrast Enhancement of Dental X-ray images

Dental radiography is essential for diagnosing and planning treatment of dental and alveolar conditions, utilizing modalities like 2D digital X-rays and advanced 3D imaging such as Cone Beam Computed Tomography (CBCT). While low-dose imaging techniques reduce patient radiation exposure, they pose challenges such as increased noise and reduced contrast, which can compromise diagnostic accuracy. This review categorizes contrast enhancement methods into traditional, multiscale, and AI-based approaches, highlighting their strengths and limitations. Traditional methods like Histogram Equalization (HE) and Contrast-Limited Adaptive Histogram Equalization (CLAHE) are computationally efficient but struggle with noise amplification, while multiscale techniques like Retinex and wavelet-based methods offer better detail preservation at the cost of complexity. AI-based methods, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), show significant promise in enhancing low-dose dental images but require substantial computational resources and tailored datasets. Future research should focus on developing hybrid approaches, noiseadaptive techniques, and dental-specific datasets to address the unique challenges of low-dose imaging and improve diagnostic outcomes.