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Denoising and Contrast Enhancement in Dental Radiography
Abstract
This chapter shows how large improvement in image quality can be obtained when radiographs are filtered using adequate statistical models. In particular, it shows that impulsive noise, which appears as random patterns of light and dark pixels on raw radiographs, can be efficiently removed. A switching median filter is used to this aim: failed pixels are identified first and then corrected through local median filtering. The critical stage is the correct identification of the failed pixels. We show here that a great improvement can be obtained considering an adequate sensor model and a principled noise distribution, constituted of a mixture of photon counting and impulsive noise with uniform distribution. It is then shown that contrast in cephalometric images can be largely increased using different grey levels stretching for bone and soft tissues. The two tissues are identified through an adequate mixture derived from histogram analysis, composed of two Gaussians and one inverted log-normal. Results show that both soft and bony tissues become clearly visible in the same image under a wider range of conditions. Both filters work in quasi-real time for images larger than five Mega-pixels.
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