A new heterogeneous neural network model and its application in image enhancement

• DOI: 10.1016/j.neucom.2021.01.133
• Publication Date: 2021-02-21T06:23:54Z
• Authors: Yunliang Qi; Zhen Yang; Jing Lian; Yanan Guo; Wenhao Sun; Jizhao Liu; Runze Wang; Yide Ma
• Keywords: Edge enhancement

Abstract Based on visual cortical theory of Rybak, a new heterogeneous Rybak neural network (HRYNN) model is proposed for image enhancement. HRYNN is constructed with several Rybak neural network (RYNN) models proposed, which have different parameters corresponding to different neurons. We show that HRYNN can better represent prior information for edge detail enhancement than the logarithmic domain. To capture different resolution texture features of image, a novel receptive field model is proposed to solve the problem of detail enhancement. HRYNN model has excellent enhancement effect on the edge details of image based on the receptive field’s lateral inhibitory characteristics. Moreover, the experimental enhancement results of the colour images from Berkeley image Dataset show the validity and efficiency of the proposed enhancement method. Finally, three evaluation indicators are employed to measure the enhancement result.