# Densely Residual Laplacian Super-Resolution

## TL;DR

The paper introduces the Densely Residual Laplacian Network (DRLN), a compact super-resolution model that effectively captures multi-scale features and dependencies, outperforming existing methods in quality and efficiency.

## Contribution

It proposes a novel DRLN architecture with cascading residuals, deep supervision, and Laplacian attention, enhancing feature learning for image super-resolution.

## Key findings

- DRLN outperforms state-of-the-art methods in quantitative metrics.
- The model demonstrates superior qualitative visual results.
- Efficient learning from low-resolution and noisy images is achieved.

## Abstract

Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN). The proposed network employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features. In addition, deep supervision is achieved via the densely concatenated residual blocks settings, which also helps in learning from high-level complex features. Moreover, we propose Laplacian attention to model the crucial features to learn the inter and intra-level dependencies between the feature maps. Furthermore, comprehensive quantitative and qualitative evaluations on low-resolution, noisy low-resolution, and real historical image benchmark datasets illustrate that our DRLN algorithm performs favorably against the state-of-the-art methods visually and accurately.