# CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement

arXiv: https://arxiv.org/abs/2407.07056 · HTML: https://mlsanity.com/paper/2407.07056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement](https://mlsanity.com/paper/2303.06705/) (2023)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks](https://mlsanity.com/paper/1710.01992/) (2017)
- [Low-Light Image Enhancement with Wavelet-based Diffusion Models](https://mlsanity.com/paper/2306.00306/) (2023)
- [Towards Flexible Blind JPEG Artifacts Removal](https://mlsanity.com/paper/2109.14573/) (2021)
- [On Efficient Transformer-Based Image Pre-training for Low-Level Vision](https://mlsanity.com/paper/2112.10175/) (2021)
- [FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information](https://mlsanity.com/paper/2308.03033/) (2023)
- [Quantization Guided JPEG Artifact Correction](https://mlsanity.com/paper/2004.09320/) (2020)
- [Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement](https://mlsanity.com/paper/2203.01296/) (2022)
- [Brighten-and-Colorize: A Decoupled Network for Customized Low-Light Image Enhancement](https://mlsanity.com/paper/2308.03029/) (2023)

## Built on by (most cited first, 1 of 1)

- [HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement](https://mlsanity.com/paper/2504.02373/) (2025)

## Abstract (arXiv)

Low-Light Image Enhancement (LLIE) has advanced with the surge in phone photography demand, yet many existing methods neglect compression, a crucial concern for resource-constrained phone photography. Most LLIE methods overlook this, hindering their effectiveness. In this study, we investigate the effects of JPEG compression on low-light images and reveal substantial information loss caused by JPEG due to widespread low pixel values in dark areas. Hence, we propose the Compression-Aware Pre-trained Transformer (CAPformer), employing a novel pre-training strategy to learn lossless information from uncompressed low-light images. Additionally, the proposed Brightness-Guided Self-Attention (BGSA) mechanism enhances rational information gathering. Experiments demonstrate the superiority of our approach in mitigating compression effects on LLIE, showcasing its potential for improving LLIE in resource-constrained scenarios.