# Image Super-Resolution via Attention based Back Projection Networks

arXiv: https://arxiv.org/abs/1910.04476 · HTML: https://mlsanity.com/paper/1910.04476/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 16 of 16)

- [Learning Frequency-aware Dynamic Network for Efficient Super-Resolution](https://mlsanity.com/paper/2103.08357/) (2021)
- [Revisiting Temporal Alignment for Video Restoration](https://mlsanity.com/paper/2111.15288/) (2021)
- [EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset](https://mlsanity.com/paper/2312.08220/) (2023)
- [Variational AutoEncoder for Reference based Image Super-Resolution](https://mlsanity.com/paper/2106.04090/) (2021)
- [IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image](https://mlsanity.com/paper/2103.03480/) (2021)
- [Multiple Style Transfer via Variational AutoEncoder](https://mlsanity.com/paper/2110.07375/) (2021)
- [Deep Spatio-Temporal Neural Network for Air Quality Reanalysis](https://mlsanity.com/paper/2502.11941/) (2025)
- [Feedback Pyramid Attention Networks for Single Image Super-Resolution](https://mlsanity.com/paper/2106.06966/) (2021)
- [Laplacian Pyramid-like Autoencoder](https://mlsanity.com/paper/2208.12484/) (2022)
- [See360: Novel Panoramic View Interpolation](https://mlsanity.com/paper/2401.03431/) (2024)
- [Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement](https://mlsanity.com/paper/2103.01698/) (2021)
- [Soft-IntroVAE for Continuous Latent space Image Super-Resolution](https://mlsanity.com/paper/2307.09008/) (2023)
- [Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising Model](https://mlsanity.com/paper/2311.02358/) (2023)
- [Downscaling Neural Network for Coastal Simulations](https://mlsanity.com/paper/2408.16553/) (2024)
- [PUFM++: Point Cloud Upsampling via Enhanced Flow Matching](https://mlsanity.com/paper/2512.20988/) (2025)
- [Detail Loss in Super-Resolution Models Based on the Laplacian Pyramid and Repeated Upscaling and Downscaling Process](https://mlsanity.com/paper/2601.09410/) (2026)

## Abstract (arXiv)

Deep learning based image Super-Resolution (SR) has shown rapid development due to its ability of big data digestion. Generally, deeper and wider networks can extract richer feature maps and generate SR images with remarkable quality. However, the more complex network we have, the more time consumption is required for practical applications. It is important to have a simplified network for efficient image SR. In this paper, we propose an Attention based Back Projection Network (ABPN) for image super-resolution. Similar to some recent works, we believe that the back projection mechanism can be further developed for SR. Enhanced back projection blocks are suggested to iteratively update low- and high-resolution feature residues. Inspired by recent studies on attention models, we propose a Spatial Attention Block (SAB) to learn the cross-correlation across features at different layers. Based on the assumption that a good SR image should be close to the original LR image after down-sampling. We propose a Refined Back Projection Block (RBPB) for final reconstruction. Extensive experiments on some public and AIM2019 Image Super-Resolution Challenge datasets show that the proposed ABPN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.