Content-aware neuron image enhancement
Neuron microscopy enables advances in the exploration of neural anatomy and function, and neuron image enhancement is typically necessary before higher-level analyses. Most existing neuron image enhancement methods exploit the unique tubular structure of neurons. However, such methods do not preserve neuron details. In this paper, we propose a content-aware neuron image enhancement (CaNE) method that combines the neuron image properties of gradient sparsity and tubular structure. Experiments show that neuron images enhanced by CaNE possess 20% more details in terms of correlation as compared to existing methods. With high-quality neuron images provided by CaNE, neuron analyses, such as segmentation and skeleton detection, are enabled at higher rates of success as described in this paper.