We introduce an adaptive Gaussian-Cauchy mixture modeling for the likelihood of pairwise mean/standard-deviation scatter points found when estimating signal-dependent noise. The maximization of the likelihood is used to identify the noise-model parameters, following an adaptive mixture parameter that controls the balance between the Gaussian and the heavy-tailed Cauchy. This renders the estimation robust with respect to outliers, typically present in large quantities among the scatter points from images dominated by texture. The modeling is directly suited to describing also observations subject to clipping, i.e. under-or over-exposure. Experiments on a dataset of badly exposed and highly textured images demonstrate the effectiveness of the adaptive GaussianCauchy mixture likelihood for the accurate estimation of the noise standard-deviation curve.
In breast cancer screening, the radiation dose must be kept to the minimum necessary to achieve the desired diagnostic objective, thus minimizing risks associated with cancer induction. However, decreasing the radiation dose also degrades the image quality. In this work we restore digital breast tomosynthesis (DBT) projections acquired at low radiation doses with the goal of achieving a quality comparable to that obtained from current standard full-dose imaging protocols. A multiframe denoising algorithm was applied to low-dose projections, which are filtered jointly. Furthermore, a weighted average was used to inject a varying portion of the noisy signal back into the denoised one, in order to attain a signal-to-noise ratio comparable to that of standard full-dose projections. The entire restoration framework leverages a signal-dependent noise model with quantum gain which varies both upon the projection angle and on the pixel position. A clinical DBT system and a 3D anthropomorphic breast phantom were used to validate the proposed method, both on DBT projections and slices from the 3D reconstructed volume. The framework is shown to attain the standard full-dose image quality from data acquired at 50% lower radiation dose, whereas progressive loss of relevant details compromises the image quality if the dosage is further decreased.
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