# Gradient pooling distillation network for lightweight single image super-resolution reconstruction

## TL;DR

This paper introduces a new lightweight deep learning model for improving low-resolution images while using minimal computing resources.

## Contribution

The novel Gradient Pooling Distillation Network (GPDN) balances high image quality with low computational demands.

## Key findings

- The GPDN achieves competitive performance in image super-resolution with low resource usage.
- The model uses multi-level feature distillation and channel attention to enhance high-resolution image recovery.
- Experiments show the method effectively balances quality and efficiency for real-world applications.

## Abstract

The single image super-resolution (SISR) is a classical problem in the field of computer vision, aiming to enhance high-resolution details from low-resolution images. In recent years, significant progress about SISR has been achieved through the utilization of deep learning technology. However, these deep methods often exhibit large-scale networks architectures, which are computationally intensive and hardware-demanding, and this limits their practical application in some scenarios (e.g., autonomous driving, streaming media) requiring stable and efficient image transmission with high-definition picture quality. In such application settings, computing resources are often restricted. Thus, there is a pressing demand to devise efficient super-resolution algorithms. To address this issue, we propose a gradient pooling distillation network (GPDN), which can enable the efficient construction of a single image super-resolution system. In the GPDN we leverage multi-level stacked feature distillation hybrid units to capture multi-scale feature representations, which are subsequently synthesized for dynamic feature space optimization. The central to the GPDN is the Gradient Pooling Distillation module, which operates through hierarchical pooling to decompose and refine critical features across various dimensions. Furthermore, we introduce the Feature Channel Attention module to accurately filter and strengthen pixel features crucial for recovering high-resolution images. Extensive experimental results demonstrate that our proposed method achieves competitive performance while maintaining relatively low resource occupancy of the model. This model strikes for a balance between excellent performance and resource utilization—particularly when trading off high recovery quality with small memory occupancy.