Convolutional Neural Network algorithm is leading technic for image denoising. Usually CNNs are used on pair of images, but it is impractical and difficult to have clean image or pair of denoised images. Noise2Void method uses a single image for training CNN to predict intensity distribution for each pixel. Using these with suitable desciption of noise a complete probablistic mode is obtained for noisy observation and true signal in each pixel. To obtain the posterior distribution for each single pixel, combintion of a general noise model that can be represented as a histogram (observation likelihood), and a distribution of possible true pixel intensities (prior), represented by a set of predicted samples is used. Minimal Mean Squared Error (MMSE) estimators are used to get the final predictions and it is shown that MMSE-PN2V consistently outperformes other self-supervised methods.
Input variables : Noisy Microscopic Image
Output Variables : Denoised Images
Visit Model : github.com
Additional links : frontiersin.org
| Model Category | : | Public |
| Date Published | : | February, 2020 |
| Healthcare Domain | : | Medical Technology |
| Code | : | github.com |
| Medical Imaging |
| Image Denoising |