Lightweight image super-resolution network using 3D convolutional neural networks

Abstract

In recent years, significant progress has been made in single-image super-resolution (SISR) with the emergence of convolutional neural networks (CNNs). However, the application of SISR on low computing power devices is hindered by the massive number of parameters and computational costs. Despite the focus on lightweight SISR models in many studies, the majority still struggles to balance performance and model size, making it difficult to apply them in real-life situations. Therefore, we propose to construct an SISR network termed 3D lightweight image super-resolution (3DLSR) network by introducing 3DCNN to this task. By leveraging the additional dimension of 3D convolution, the proposed 3DLSR can extract the interchannel and innerchannel information of color images, thereby aiding the reconstruction of high-resolution images while maintaining a small model size. Furthermore, we redesign a best-fitting network structure for 3DLSR based on the difference between 3D convolution and 2D convolution. The experimental results demonstrate the superiority of our 3DLSR, as it can achieve a competitively quantitative metric with a parameter size one order of magnitude smaller than the majority, compared with the state-of-the-art methods.