# CE-VAE: Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement

arXiv: https://arxiv.org/abs/2406.01294 · HTML: https://mlsanity.com/paper/2406.01294/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](https://mlsanity.com/paper/1811.01343/) (2018)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception](https://mlsanity.com/paper/2002.01155/) (2020)

## Built on by (most cited first, 4 of 4)

- [FlowLUT: Efficient Image Enhancement via Differentiable LUTs and Iterative Flow Matching](https://mlsanity.com/paper/2509.23608/) (2025)
- [Physics Informed Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement](https://mlsanity.com/paper/2506.04753/) (2025)
- [RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision](https://mlsanity.com/paper/2606.06176/) (2026)
- [Fusing Transferred Priors and Physics-based Decomposition for Underwater Image Enhancement](https://mlsanity.com/paper/2606.15648/) (2026)

## Abstract (arXiv)

Unmanned underwater image analysis for marine monitoring faces two key challenges: (i) degraded image quality due to light attenuation and (ii) hardware storage constraints limiting high-resolution image collection. Existing methods primarily address image enhancement with approaches that hinge on storing the full-size input. In contrast, we introduce the Capsule Enhanced Variational AutoEncoder (CE-VAE), a novel architecture designed to efficiently compress and enhance degraded underwater images. Our attention-aware image encoder can project the input image onto a latent space representation while being able to run online on a remote device. The only information that needs to be stored on the device or sent to a beacon is a compressed representation. There is a dual-decoder module that performs offline, full-size enhanced image generation. One branch reconstructs spatial details from the compressed latent space, while the second branch utilizes a capsule-clustering layer to capture entity-level structures and complex spatial relationships. This parallel decoding strategy enables the model to balance fine-detail preservation with context-aware enhancements. CE-VAE achieves state-of-the-art performance in underwater image enhancement on six benchmark datasets, providing up to 3x higher compression efficiency than existing approaches. Code available at urlhttps://github.com/iN1k1/ce-vae-underwater-image-enhancement.