Convolutional Neural Networks Using Skip Connections with Layer Groups for Super-Resolution Image Reconstruction Based on Deep Learning

Applied Sciences

Hyeongyeom Ahn¹  Changhoon Yim¹ 

[1] Intelligent Image Processing Laboratory, Konkuk University, Seoul 05029, Korea;

关键词: convolutional neural networks;    deep learning;    super-resolution;    image reconstruction;    skip connection;    layer group;   

DOI  :  10.3390/app10061959

来源: DOAJ

【 摘 要 】

In this paper, we propose a deep learning method with convolutional neural networks (CNNs) using skip connections with layer groups for super-resolution image reconstruction. In the proposed method, entire CNN layers for residual data processing are divided into several layer groups, and skip connections with different multiplication factors are applied from input data to these layer groups. With the proposed method, the processed data in hidden layer units tend to be distributed in a wider range. Consequently, the feature information from input data is transmitted to the output more robustly. Experimental results show that the proposed method yields a higher peak signal-to-noise ratio and better subjective quality than existing methods for super-resolution image reconstruction.

【 授权许可】

Unknown