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[bibtex]@InProceedings{Pucci_2025_WACV, author = {Pucci, Rita and Martinel, Niki}, title = {CE-VAE: Capsule Enhanced Variational AutoEncoderfor Underwater Image Enhancement}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {2113-2123} }
CE-VAE: Capsule Enhanced Variational AutoEncoderfor Underwater Image Enhancement
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 3x higher compression efficiency than existing approaches. Code available at https://github.com/iN1k1/ce-vae-underwater-image-enhancement.
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