Feature Linking Model for Image Enhancement

K. Zhan, et al., Feature-linking model for image enhancement, Neural Computation, 2016

Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis towards temporal coding, which uses explicit times at which spikes occur as an essential dimension in neural representations. We present a feature linking model (FLM) that uses the timing of spikes to encode information. The first spiking time of FLM is applied to image enhancement, and the processing mechanisms are consistent with human visual system. The enhancement algorithm achieves boosting the details while preserving the information of the input image. Experiments are conducted to demonstrate the effectiveness of the proposed method. Results show that the proposed method is effective.
Reference:
K. Zhan, J. Shi, J. Teng, Q. Li and M. Wang,
Feature-linking model for image enhancement,
Neural Computation, vol. 28, no. 6, pp. 1072-1100, 2016.
http://www.escience.cn/people/kzhan