Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network

TL;DR

This paper introduces a fast, efficient deep CNN model for single image super-resolution that achieves state-of-the-art results with significantly reduced computational cost, suitable for edge devices.

Contribution

The authors propose a novel Deep CNN architecture with residual, skip, and Network in Network layers that balances high performance and low computation for super-resolution.

Findings

Achieves state-of-the-art super-resolution performance.

At least 10 times lower computation cost than existing models.

Suitable for deployment on resource-constrained devices.

Abstract

We propose a highly efficient and faster Single Image Super-Resolution (SISR) model with Deep Convolutional neural networks (Deep CNN). Deep CNN have recently shown that they have a significant reconstruction performance on single-image super-resolution. Current trend is using deeper CNN layers to improve performance. However, deep models demand larger computation resources and is not suitable for network edge devices like mobile, tablet and IoT devices. Our model achieves state of the art reconstruction performance with at least 10 times lower calculation cost by Deep CNN with Residual Net, Skip Connection and Network in Network (DCSCN). A combination of Deep CNNs and Skip connection layers is used as a feature extractor for image features on both local and global area. Parallelized 1x1 CNNs, like the one called Network in Network, is also used for image reconstruction. That structure reduces the dimensions of the previous layer's output for faster computation with less information loss, and make it possible to process original images directly. Also we optimize the number of layers and filters of each CNN to significantly reduce the calculation cost. Thus, the proposed algorithm not only achieves the state of the art performance but also achieves faster and efficient computation. Code is available at https://github.com/jiny2001/dcscn-super-resolution