288 pp. per issue
6 x 9, illustrated
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Neural Computation

June 2016, Vol. 28, No. 6, Pages 1072-1100
(doi: 10.1162/NECO_a_00832)
© 2016 Massachusetts Institute of Technology
Feature-Linking Model for Image Enhancement
Article PDF (1.35 MB)

Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis toward 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 the 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.