Abstract: Method for determining the concentrations of constituent chemical species in a mixture, including the steps of: using nuclear magnetic resonance spectroscopy, acquiring an NMR measurement for a sample of the mixture; and for each of the constituent chemical species, retrieving a reference model representative of the NMR FID signal or frequency domain spectra from a database. Each model has a number of parameters, and for at least one of the constituent chemical species, the reference model is a quantum mechanical model. The method further includes, using a computer, generating a model signal for the mixture and adjusting some or all of the model parameters to fit the model signal to the measured data; and based on the fitted model signal, calculating and displaying the concentrations of the constituent species in the sample.

Abstract: A computer-implemented method for denoising image data includes a computer system receiving an input image comprising noisy image data and denoising the input image using a deep multi-scale network comprising a plurality of multi-scale networks sequentially connected. Each respective multi-scale network performs a denoising process which includes dividing the input image into a plurality of image patches and denoising those image patches over multiple levels of decomposition using a threshold-based denoising process. The threshold-based denoising process denoises each respective image patch using a threshold which is scaled according to an estimation of noise present in the respective image patch. The noising process further comprises the assembly of a denoised image by averaging over the image patches.

Abstract: A computer-implemented method for denoising image data includes a computer system receiving an input image comprising noisy image data and denoising the input image using a deep multi-scale network comprising a plurality of multi-scale networks sequentially connected. Each respective multi-scale network performs a denoising process which includes dividing the input image into a plurality of image patches and denoising those image patches over multiple levels of decomposition using a threshold-based denoising process. The threshold-based denoising process denoises each respective image patch using a threshold which is scaled according to an estimation of noise present in the respective image patch. The noising process further comprises the assembly of a denoised image by averaging over the image patches.