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

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2113–2123

Abstract

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 3 × higher compression efficiency than existing approaches. Code available at https://github.com/iN1k1/ce-vae-underwater-image-enhancement.

Authors 2

1. Rita Pucci Aachen Naturalis Biodiversity Center

   Naturalis Biodiversity Center

   Affiliation as printed

   Naturalis Biodiversity Center,Leiden,Netherlands

2. Niki Martinel

   University of Udine

   Affiliation as printed

   University of Udine,Udine,Italy