Improvement of Dental X-rays Images using Image Processing Techniques

This thesis proposed a procedure for effective statistical noise analysis in dental rays. A Gaussian scale blend has been used to satisfy non-linearity dispersion to improve the quality of images after denoisation. Current methods are based on a filter based on insignificant details. The traditional theory of gaseous and poison diffusion seems only to overestimate noise variance in low-intensity areas (small photon counts). 20 experiments from 20 panorama pictures test the retrospective method, and medical experts back the findings. Secondly, the general image has been maintained; secondly, the diagnostic data on the photo are preserved, and thirdly, the scene diagnostics section defines small details of low contrast. As illustrated above, state-of - the-art approaches have poor results. This new approach is followed by an attempt to see the problem from BSS ' perspective in order to see the panorama as a simple combination of (unwanted) background, observational and noise information.