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Panoramic dental radiography is an essential tool in dental diagnostics, as it allows for a comprehensive assessment of bone structure and dental alignment. However, image quality can be affected by noise and low contrast, making precise interpretation challenging. This study addresses this limitation by proposing an innovative methodology based on advanced contrast enhancement techniques to improve the visual quality of radiographic images. The Wiener filter is applied to reduce the noise in the original image. Then, the filtered image undergoes various contrast enhancement techniques to improve the visibility of relevant structures. Finally, specialized metrics evaluate the results to quantify contrast, spatial information, and image details. The findings show that the proposed method produces images with excellent sharpness, improved contrast, and preserved brightness while maintaining a high structural similarity to the original image. The proposed methodology analyzes 598 radiographic images, significantly improving visual perception and diagnostic accuracy.

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A Hybrid Algorithm for the Enhancement of Panoramic Radiographs of Teeth Using the Wiener Filter and Morphological Contrast Operators

  • 会议论文

  • 发表日期：

    2026年

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  • Jesus Ariel López,
  • Walter Natalicio,
  • Julio César Mello-Román,
  • Diego Pinto-Roa,
  • Edher Herrera,
  • José Luis Vázquez Noguera,
  • Miguel García-Torres,
  • Horacio Legal-Ayala,
  • Vicente Fretes López,
  • Carlos G. Adorno,
  • Gabriela Cuevas,
  • Julieta Maria Mendez Romero,
  • Ulises Armando Villasanti Torales

摘要

Panoramic dental radiography is an essential tool in dental diagnostics, as it allows for a comprehensive assessment of bone structure and dental alignment. However, image quality can be affected by noise and low contrast, making precise interpretation challenging. This study addresses this limitation by proposing an innovative methodology based on advanced contrast enhancement techniques to improve the visual quality of radiographic images. The Wiener filter is applied to reduce the noise in the original image. Then, the filtered image undergoes various contrast enhancement techniques to improve the visibility of relevant structures. Finally, specialized metrics evaluate the results to quantify contrast, spatial information, and image details. The findings show that the proposed method produces images with excellent sharpness, improved contrast, and preserved brightness while maintaining a high structural similarity to the original image. The proposed methodology analyzes 598 radiographic images, significantly improving visual perception and diagnostic accuracy.