Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
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Verifying that each radiation beam is being delivered as intended constitutes a fundamental issue in radiation therapy. In order to verify the patient positioning the own high energy radiation beam is commonly used to produce an image similar to a radiography (portal image), which is further compared to an image generated in the simulation-planning phase. The evolution of radiotherapy is parallel to the increase of the number of portal images used for each treatment, and as a consecuence the radiation dose due to image has increased notably. The concern arises from the fact that the radiation delivered during imaging is not confined to the treatment volumes. One possible solution should be the reduction of dose per image, but the image quality should become lower as the quantum noise should become higher. The limited quality of portal images makes difficult to propose dose reduction if there is no way to deal with noise increment. In this work we study the denoising of portal images by various denoising algorithms. In particular we are interested in wavelet-based denoising. The wavelet-based algorithms used are the shrinkage by wavelet coefficients thresholding, the coefficient extraction based on correlation between wavelet scales and the Bayesian least squares estimate of wavelet coefficients with Gaussians scale mixtures as priors (BLS-GSM). Two algorithms that do not use wavelets are also evaluated, a local Wiener estimator and the Non Local Means algorithm (NLM). We found that wavelet thresholding, wavelet coefficients extraction after correlation and NLM reach higher values of ISNR than Local Wiener. Also, the highest ISNR is reached by the BLS-GSM algorithm. This algorithm also produces the best visual results. We believe that these results are very encouraging for exploring forms of reducing the radiation doses associated to portal image in radiotherapy.