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Merging AI and underwater images to disclose hidden ocean worlds | MIT Information

Admin by Admin
June 26, 2025
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Within the Northeastern United States, the Gulf of Maine represents some of the biologically various marine ecosystems on the planet — residence to whales, sharks, jellyfish, herring, plankton, and lots of of different species. However at the same time as this ecosystem helps wealthy biodiversity, it’s present process fast environmental change. The Gulf of Maine is warming quicker than 99 p.c of the world’s oceans, with penalties which can be nonetheless unfolding.

A brand new analysis initiative creating at MIT Sea Grant, referred to as LOBSTgER — quick for Studying Oceanic Bioecological Programs By Generative Representations — brings collectively synthetic intelligence and underwater images to doc the ocean life left weak to those modifications and share them with the general public in new visible methods. Co-led by underwater photographer and visiting artist at MIT Sea Grant Keith Ellenbogen and MIT mechanical engineering PhD pupil Andreas Mentzelopoulos, the mission explores how generative AI can broaden scientific storytelling by constructing on field-based photographic information.

Simply because the Nineteenth-century digicam reworked our capacity to doc and reveal the pure world — capturing life with unprecedented element and bringing distant or hidden environments into view — generative AI marks a brand new frontier in visible storytelling. Like early images, AI opens a artistic and conceptual area, difficult how we outline authenticity and the way we talk scientific and creative views. 

Within the LOBSTgER mission, generative fashions are skilled solely on a curated library of Ellenbogen’s authentic underwater pictures — every picture crafted with creative intent, technical precision, correct species identification, and clear geographic context. By constructing a high-quality dataset grounded in real-world observations, the mission ensures that the ensuing imagery maintains each visible integrity and ecological relevance. As well as, LOBSTgER’s fashions are constructed utilizing customized code developed by Mentzelopoulos to guard the method and outputs from any potential biases from exterior information or fashions. LOBSTgER’s generative AI builds upon actual images, increasing the researchers’ visible vocabulary to deepen the general public’s connection to the pure world.

A photoreal image of a large oval ocean sunfish underwater. An orange LOBSTgER icon indicates this was made with AI.

This ocean sunfish (Mola mola) picture was generated by LOBSTgER’s unconditional fashions.

AI-generated picture: Keith Ellenbogen, Andreas Mentzelopoulos, and LOBSTgER.

At its coronary heart, LOBSTgER operates on the intersection of artwork, science, and know-how. The mission attracts from the visible language of images, the observational rigor of marine science, and the computational energy of generative AI. By uniting these disciplines, the group just isn’t solely creating new methods to visualise ocean life — they’re additionally reimagining how environmental tales will be informed. This integrative method makes LOBSTgER each a analysis instrument and a artistic experiment — one which displays MIT’s long-standing custom of interdisciplinary innovation.

Underwater images in New England’s coastal waters is notoriously troublesome. Restricted visibility, swirling sediment, bubbles, and the unpredictable motion of marine life all pose fixed challenges. For the previous a number of years, Ellenbogen has navigated these challenges and is constructing a complete file of the area’s biodiversity by way of the mission, House to Sea: Visualizing New England’s Ocean Wilderness. This massive dataset of underwater photographs gives the inspiration for coaching LOBSTgER’s generative AI fashions. The photographs span various angles, lighting situations, and animal behaviors, leading to a visible archive that’s each artistically placing and biologically correct.

Picture synthesis through reverse diffusion: This quick video reveals the de-noising trajectory from Gaussian latent noise to photorealistic output utilizing LOBSTgER’s unconditional fashions. Iterative de-noising requires 1,000 ahead passes by way of the skilled neural community.

Video: Keith Ellenbogen and Andreas Mentzelopoulos / MIT Sea Grant

LOBSTgER’s customized diffusion fashions are skilled to duplicate not solely the biodiversity Ellenbogen paperwork, but additionally the creative fashion he makes use of to seize it. By studying from 1000’s of actual underwater photographs, the fashions internalize fine-grained particulars comparable to pure lighting gradients, species-specific coloration, and even the atmospheric texture created by suspended particles and refracted daylight. The result’s imagery that not solely seems visually correct, but additionally feels immersive and transferring.

The fashions can each generate new, artificial, however scientifically correct photographs unconditionally (i.e., requiring no person enter/steerage), and improve actual pictures conditionally (i.e., image-to-image era). By integrating AI into the photographic workflow, Ellenbogen will be capable of use these instruments to get well element in turbid water, alter lighting to emphasise key topics, and even simulate scenes that may be practically unattainable to seize within the subject. The group additionally believes this method might profit different underwater photographers and picture editors going through related challenges. This hybrid methodology is designed to speed up the curation course of and allow storytellers to assemble a extra full and coherent visible narrative of life beneath the floor.

Side-by-side images of an American lobster on the sea floor underneath seaweed. One has been enhanced by AI and is far more vibrant.

Left: Enhanced picture of an American lobster utilizing LOBSTgER’s image-to-image fashions. Proper: Authentic picture.

Left: AI genertated picture by Keith Ellenbogen, Andreas Mentzelopoulos, and LOBSTgER. Proper: Keith Ellenbogen

In a single key collection, Ellenbogen captured high-resolution photographs of lion’s mane jellyfish, blue sharks, American lobsters, and ocean sunfish (Mola mola) whereas free diving in coastal waters. “Getting a high-quality dataset just isn’t straightforward,” Ellenbogen says. “It requires a number of dives, missed alternatives, and unpredictable situations. However these challenges are a part of what makes underwater documentation each troublesome and rewarding.”

Mentzelopoulos has developed authentic code to coach a household of latent diffusion fashions for LOBSTgER grounded on Ellenbogen’s photographs. Growing such fashions requires a excessive stage of technical experience, and coaching fashions from scratch is a posh course of demanding lots of of hours of computation and meticulous hyperparameter tuning.

The mission displays a parallel course of: subject documentation by way of images and mannequin improvement by way of iterative coaching. Ellenbogen works within the subject, capturing uncommon and fleeting encounters with marine animals; Mentzelopoulos works within the lab, translating these moments into machine-learning contexts that may lengthen and reinterpret the visible language of the ocean.

“The objective isn’t to exchange images,” Mentzelopoulos says. “It’s to construct on and complement it — making the invisible seen, and serving to individuals see environmental complexity in a approach that resonates each emotionally and intellectually. Our fashions purpose to seize not simply organic realism, however the emotional cost that may drive real-world engagement and motion.”

LOBSTgER factors to a hybrid future that merges direct commentary with technological interpretation. The group’s long-term objective is to develop a complete mannequin that may visualize a variety of species discovered within the Gulf of Maine and, finally, apply related strategies to marine ecosystems world wide.

The researchers counsel that images and generative AI kind a continuum, somewhat than a battle. Pictures captures what’s — the feel, mild, and animal conduct throughout precise encounters — whereas AI extends that imaginative and prescient past what’s seen, towards what might be understood, inferred, or imagined primarily based on scientific information and creative imaginative and prescient. Collectively, they provide a strong framework for speaking science by way of image-making.

In a area the place ecosystems are altering quickly, the act of visualizing turns into extra than simply documentation. It turns into a instrument for consciousness, engagement, and, in the end, conservation. LOBSTgER remains to be in its infancy, and the group appears to be like ahead to sharing extra discoveries, photographs, and insights because the mission evolves.

Reply from the lead picture: The left picture was generated utilizing utilizing LOBSTgER’s unconditional fashions and the precise picture is actual.

For extra data, contact Keith Ellenbogen and Andreas Mentzelopoulos.

Tags: hiddenMergingMITNewsoceanphotographyRevealunderwaterWorlds
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