Content-aware-Neuron-Image-Enhancement

Content-aware neuron image enhancement (CaNE) is an image enhancement algorithm for images with filamentous structures.

Introduction

By exploring the property of sparsity and tube-like structure in the neuron images, we formulate high quality neuron images with a cost function. By minimizing the cost function iteratively, clutters and noise in neuron images are gradually removed. For more details about this work, please refer to our publications [1,2].

1. Content-aware Neuron Image Enhancement, Haoyi Liang, Scott Acton and Daniel Weller, IEEE International Conf. on Image Processing, pp. 3510-3514, 2017
2. Content-Aware Enhancement of Images with Filamentous Structures, Haris Jeelani, Haoyi Liang, Scott Acton and Daniel Weller, IEEE Trans. on Image Processing, 2019

2D example
The left image is the input data, and the right one is the CaNE output.

3D example
This animation demonstrates how CaNE removes background clutters for 3D data.

Dependencies

1. numpy: matrix operation. Installation: pip insall numpy
2. imageio: Read and write image data. Installation: pip install imageio