Objective factors lead to poor contrast and blurred edges of the lesion area in eye optical coherence tomography （OCT） images obtained clinically. To address these issues， a super-resolution reconstruction method for single eye OCT image， named EOTRN， is proposed by referring to the information processing mechanism of the eagle vision system. It imitates the idea of gradually expanding the receptive field of the eagle optic tectum， and excavates advanced semantic features from both the vertical and horizontal dimensions step by step. In the vertical dimension， EOTRN utilizes dilated convolutions， dense connection and a channel attention mechanism to gradually expand the receptive field. This process propagates the characteristics of different network layers， enabling competition or cooperation among different channel features. As a result， the preliminary extraction of advanced semantic features of low-frequency signals is achieved. In the horizontal dimension， EOTRN eliminates redundant information from the advanced semantic features， corrects and highlights salient information， and enhances the texture and contour features of the lesion areas using 64 characteristic subspaces. Finally， the underlying semantic features and the advanced semantic features are upsampled and deeply reconstructed to obtain high-definition images. the Experiments show that， for the third test set with scale factor ×4， compared with EMASRN， EOTRN achieves 0.96% increase in PSNR and 1.36% increase in SSIM values. The reconstructed images generated by EOTRN effectively highlight the detailed information and accurately reflect the health of the fundus. Moreover， EOTRN has fewer parameters， making it suitable for the deployment of embedded systems to realize real-time ultra-clear reconstruction of eye OCT images.