ﻻ يوجد ملخص باللغة العربية
Image registration is the inference of transformations relating noisy and distorted images. It is fundamental in computer vision, experimental physics, and medical imaging. Many algorithms and analyses exist for inferring shift, rotation, and nonlinear transformations between image coordinates. Even in the simplest case of translation, however, all known algorithms are biased and none have achieved the precision limit of the Cramer Rao bound (CRB). Following Bayesian inference, we prove that the standard method of shifting one image to match another cannot reach the CRB. We show that the bias can be cured and the CRB reached if, instead, we use Super Registration: learning an optimal model for the underlying image and shifting that to match the data. Our theory shows that coarse-graining oversampled images can improve registration precision of the standard method. For oversampled data, our method does not yield striking improvements as measured by eye. In these cases, however, we show our new registration method can lead to dramatic improvements in extractable information, for example, inferring $10times$ more precise particle positions.
Neutron direct-geometry time-of-flight chopper spectroscopy is instrumental in studying fundamental excitations of vibrational and/or magnetic origin. We report here that techniques in super-resolution optical imagery (which is in real-space) can be
Signal processing techniques have been developed that use different strategies to bypass the Nyquist sampling theorem in order to recover more information than a traditional discrete Fourier transform. Here we examine three such methods: filter diago
Two dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently unstable when the local landscape is complicated, or t
Despite of their success, the results of first-principles quantum mechanical calculations contain inherent numerical errors caused by various approximations. We propose here a neural-network algorithm to greatly reduce these inherent errors. As a dem
Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream high-to-low, low-t