Low Dose and High Contrast Biomedical Imaging Using SelfSupervised Deep Learning

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Original abstract

Self-supervised deep learning has emerged as a powerful method for image enhancement when a priori ground-truth references are not available. Stemming from Noise2Noise , it was shown that a convolutional neural network (CNN) can be trained from a noisy input and target pair of the same scene to produce clean images, given that the noise distributions are independent and image features have the same mean grey value. However, while this method has shown great promise for denoising, the majority of literature since has been largely focused on noise reduction. Addressing other prevalent artefacts in biomedical imaging, such as high-contrast visualization of soft tissues and low-dose imaging, depends on image quality beyond noise

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