{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/capsule-enhanced-variational-autoencoder-for","title":"CE-VAE: Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement","arxiv_id":"2406.01294","date":"2024-06-03","proceeding":null,"authors":["Rita Pucci","Niki Martinel"],"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 \\url{https://github.com/iN1k1/ce-vae-underwater-image-enhancement}.","url_abs":"https://arxiv.org/abs/2406.01294v2","url_pdf":"https://arxiv.org/pdf/2406.01294v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"capsule-enhanced-variational-autoencoder-for","repo_url":"https://github.com/in1k1/ce-vae-underwater-image-enhancement","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"underwater-image-restoration","task_name":"Underwater Image Restoration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/underwater-image-restoration-on-lsui","task":"Underwater Image Restoration","dataset":"LSUI","model":"CE-VAE","rank_in_archive_order":1,"of":6,"metrics":{"PSNR":"24.49"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}