Super-Resolution Imaging of the Extracellular Space in Living Brain Tissue

Robust Fuzzy Content Based Regularization Technique in Super Resolution Imaging

Super-resolution (SR) aims to overcome the ill-posed conditions of image acquisition. SR facilitates scene recognition from low-resolution image(s). Generally assumes that high and low resolution images share similar intrinsic geometries. Various approaches have tried to aggregate the informative details of multiple low-resolution images into a high-resolution one. In this paper, we present a n...

Super Resolution Imaging of Genetically Labeled Synapses in Drosophila Brain Tissue

Understanding synaptic connectivity and plasticity within brain circuits and their relationship to learning and behavior is a fundamental quest in neuroscience. Visualizing the fine details of synapses using optical microscopy remains however a major technical challenge. Super resolution microscopy opens the possibility to reveal molecular features of synapses beyond the diffraction limit. With...

Super-resolution in brain Diffusion Weighted Imaging (DWI)

Diffusion Weighted (DW) imaging has proven to be useful at analysing brain architecture as well as at establishing brain tract organization and neuronal connectivity. However, an actual clinical use of DW images is currently limited by a series of acquisition artifacts, among them the partial volume effect (PVE) that may completely alter the spatial resolution and therefore the visualization of...

Imaging-Consistent Super-Resolution

This paper introduces two algorithms for enhancing image resolution from an image sequence. The “image-based” approach presumes that the images were taken under the same illumination conditions and uses the intensity information provided by the image sequence to construct the high-resolution image. This ideal, however, is almost always not true when the illumination varies. The “edge-based” app...

Editorial: Brain Extracellular Space: Geometry, Matrix and Physiological Importance