4 papers
ShapeEmbed: a self-supervised learning framework for 2D contour quantification
Anna Foix Romero, Craig Russell, Alexander Krull +1
The shape of objects is an important source of visual information in a wide range of applications. One of the core challenges of shape quantification is to ensure that the extracte…
WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration
Xinxing Cheng, Xi Jia, Wenqi Lu +4
Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense d…
Direct Unsupervised Denoising
Benjamin Salmon, Alexander Krull
Traditional supervised denoisers are trained using pairs of noisy input and clean target images. They learn to predict a central tendency of the posterior distribution over possibl…
Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise
Benjamin Salmon, Alexander Krull
Accurate analysis of microscopy images is hindered by the presence of noise. This noise is usually signal-dependent and often additionally correlated along rows or columns of pixel…