Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
Melville Laboratory for Polymer Synthesis, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge CB2 1EW, U.K. Melville Laboratory for Polymer Synthesis, Yusuf Hamied ...
ABSTRACT: Magnetic Resonance Imaging (MRI) is commonly applied to clinical diagnostics owing to its high soft-tissue contrast and lack of invasiveness. However, its sensitivity to noise, attributable ...
Abstract: Physical-layer secret key generation (PSKG) is a well-known and effective method for boosting wireless security in the Internet of Things (IoT). This technique creates cryptographic keys ...
Image Denoising with Autoencoders in R (University Project) Built a convolutional autoencoder in R using Keras/TensorFlow to perform image denoising on MNIST and CIFAR-10 datasets with varying levels ...
Image generators are designed to mimic their training data, so where does their apparent creativity come from? A recent study suggests that it’s an inevitable by-product of their architecture. We were ...
A group of scientists led by researchers from the University of New South Wales (UNSW) in Australia has developed a novel deep-learning method for denoising outdoor electroluminescence (EL) images of ...
In PhotoniX, researchers report a self-supervised deep learning method that denoises dynamic fluorescence images in vivo without requiring clean training data. The figure shows in vivo venule images ...
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