The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation. Belief function theory, a formal framework for uncertainty analysis and multiple evidence fusion, has made significant contributions to medical image segmentation, especially since the development of deep learning. In this pape...
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This paper presents a deep neural network called DIVA unfolding a baseline adaptive denoising algorithm (De-QuIP), relying on the theory of quantum many-body physics. Furthermore, it is shown that with very slight modifications, this network can be enhanced to solve more challenging image restoration tasks such as image deblurring, super-resolution...
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Array detector allows a resolution gain for confocal microscopy by combining images sensed by a set of photomultipliers tubes (or sub-detectors). Several methods have been proposed to reconstruct a high-resolution image by linearly combining sub-detector images, especially the fluorescence emission difference (FED) technique. To improve the resolut...
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Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microRNA) data have been shown useful in tracking their ...
Screen content, which is often computer-generated, has many characteristics distinctly different from conventional camera-captured natural scene content. Such characteristic differences impose major challenges to the corresponding content quality assessment, which plays a critical role to ensure and improve the final user-perceived quality of exper...
