Zero-shot denoising of microscopy images recorded at high-resolution limits.

Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing.

Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data.

Latent Variable Models (LVMs) are well established tools to accomplish a range of different data processing tasks. Applications exploit the ability of LVMs to identify latent data structure in order to improve data (e.g.

STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds.

We investigate how the neural processing in auditory cortex is shaped by the statistics of natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives out of which sounds are composed, we employ a statistical model to extract such components. The input to the model are cochleagrams which approximate the non-linear transformations a sound undergoes from the outer ear, through the cochlea to the auditory nerve.

Extension and evaluation of a near-end listening enhancement algorithm for listeners with normal and impaired hearing.

In many applications in which speech is played back via a sound reinforcement system such as public address systems and mobile phones, speech intelligibility is degraded by additive environmental noise. A possible solution to maintain high intelligibility in noise is to pre-process the speech signal based on the estimated noise power at the position of the listener. The previously proposed AdaptDRC algorithm [Schepker, Rennies, and Doclo (2015).