Abstract
The paper considers a problem of automatic analysis and noise suppression in dental X-Ray images, e.g., in images acquired by dental Morita system. Such images contain spatially correlated noise with unknown spectrum and with standard deviation that varies for different image regions. In the paper, we propose two deep convolutional neural networks. The first network estimates the spectrum and level of noise for each pixel of a noisy image, predicting maps of noise standard deviation for three image scales. The second network uses the maps as inputs to suppress noise in the image. It is shown, using modelled and real-life images, that the proposed networks provide PSNR for dental X-Ray images by 2.7 dB better than other modern denoising methods.
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