Enhance Lab fuses a burst of raw frames into one denoised image with 4× the resolution. On the phone in under a second, on embedded GPUs, or through our cloud API.
Notre-Dame, Paris. Galaxy S25 native ×12 vs Enhance Lab ×4 on bursts from the same ×3 telephoto sensor. Drag the handle to compare.
Hand tremor shifts each frame of a burst by a fraction of a pixel. Those shifts carry the information needed to reconstruct detail beyond a single frame's pixel grid: multi-frame super-resolution.
Limited to the sensor's pixel grid, noisy in low light.
10 raw frames, each shifted by hand tremor.
Aligned and fused into one denoised, ×4 super-resolved image.
A burst of raw frames in, one image with 4× the resolution out. On the device or through our cloud API.
Works directly on the sensor's raw frames, Bayer or quad-Bayer. Sub-pixel alignment, then a learned reconstruction; a demosaicked, denoised, ×4 image out.
Runs on a phone GPU: 0.7 s for 12 MP on iPhone 16 Pro, 0.8 s for 14.5 MP on Galaxy S25. No datacenter.
One engine, two modes: multi-frame super-resolution for stills, real-time video super-resolution up to 60 fps with electronic image stabilisation, all on the device.
Nothing is generated: the output is reconstructed from the captured frames. This matters where the image is evidence.
Real FLIR footage, denoised and ×4 super-resolved with the same models. Input left, output right.
Phone native ×12 (×3 lens + ×4 digital) vs Enhance Lab ×4 on a burst from the same ×3 lens. Crops at 100%, phone left, ours right.
iPhone 15 Pro. Windows, shutter, roof, brickwork. The marked area is the crop. Full resolution, registered: phone · Enhance Lab
Galaxy S25. Clock, statues, dormer, frieze. The marked area is the crop. Full resolution, registered: phone · Enhance Lab
The same output feeds vision models. Text reconstruction (small print, engraved names), segmentation, and an OCR benchmark, shown as obtained.
155 plates, third-party detector and OCR. Plates read in full: 31.6% → 77.4%. Characters recovered: 60.7% → 91.8%.
43 plates from the public HDR+ dataset, 112 of ours. Same detector and OCR (fast-alpr) for every column.
A library for phones and embedded platforms, and a cloud API. All on-device figures measured on the device.
Qualcomm SM8750, GPU inference. Stills and live video.
| Input resolution | Output | Burst size | Run time | RAM |
|---|---|---|---|---|
| 640×480 (VGA) | 5 Mpix | 10 | 340 ms | 550 MB |
| 1280×720 (HD) | 14.5 Mpix | 10 | 790 ms | 1.4 GB |
| 1920×1080 (Full HD) | 33 Mpix | 10 | 1.75 s | 1.6 GB |
| 4064×3056 (full sensor) | 196 Mpix | 10 | 10.3 s | 3.6 GB |
Digital zoom on a region of interest, frame by frame on the GPU. EIS available at CGA, VGA, SVGA and HD.
| Input | Output | Frame rate | CPU load | GPU load, avg / peak | RAM |
|---|---|---|---|---|---|
| 96×96 | 320×200 (CGA) | 60+ fps | 4% | 15% / 16% | 156 MB |
| 176×176 | 640×480 (VGA) | 60+ fps | 6% | 24% / 27% | 170 MB |
| 208×208 | 800×600 (SVGA) | up to 60 fps | 7% | 31% / 34% | 186 MB |
| 320×320 | 1280×720 (HD) | up to 30 fps | 8% | 53% / 54% | 203 MB |
| 480×480 | 1920×1080 (Full HD) | up to 15 fps | 8% | 81% / 90% | 250 MB |
Qualcomm SM8750, OpenCL and LiteRT on the GPU; the stock camera path stays available. GPU load is temporal utilisation; CPU and RAM measured at 30 fps where the mode allows it.
Products: Image Enhancement Processor (RAW-to-RGB pipeline with ×4 zoom), Video SR (real time), SR core (the library).
×4 video super-resolution on raw video from a mirrorless camera.
Any sensor that captures more than one frame, from smartphones to thermal cameras. For people and for models.
Professor, ENS Paris and NYU.
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Research Director, Inria.
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Director, Paris School of AI, PSL University.
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Professor, NYU. Turing Award laureate.
Former Chief AI Scientist, Meta. Executive Chairman, AMI Labs.
CEO and CTO, DXOMARK.
Jean Ponce is Co-founder and CEO of Enhance Lab. He is a Professor at ENS Paris and NYU, where he chaired the ENS Computer Science Department from 2011 to 2017, and the Scientific Director and Co-founder of the PRAIRIE and PRAIRIE-PSAI institutes. He has been listed among the top 10 French computer scientists.
He is the recipient of an ERC Advanced Grant and of Test-of-Time Awards at CVPR 2016, ICML 2019 and CVPR 2020. He served as General Chair of CVPR 2000, ECCV 2008 and ICCV 2023, and as Editor-in-Chief of the International Journal of Computer Vision from 2003 to 2008 and from 2019 to 2022. He is a Fellow of AAAI, IEEE and ELLIS, and a member of the Institut Universitaire de France and of Academia Europaea.
He is a co-author of the textbook Computer Vision: A Modern Approach, with over 50,000 copies sold and translations in three languages, of the PMVS multi-view stereo software, used by ILM, Google Maps and Weta Digital, and of VICReg.
Julien Mairal is Co-founder and CSO of Enhance Lab. He is a Research Director at Inria, where he heads the THOTH team, and one of Inria's ten most cited researchers.
He is the recipient of ERC Starting and Consolidator Grants, of the IEEE PAMI Young Researcher Award and the Inria–Académie des Sciences Young Researcher Award, of a Test-of-Time Award at ICML 2019 and of the Best Paper Award at ICLR 2024.
He is a co-author of DINO, DINOv2 and DINOv3.
Isabelle Ryl is a Board Member and Strategy Advisor of Enhance Lab. She is the Director of the Paris School of AI at PSL University, a Professor at Université de Lille, and the Director and Co-founder of the PR[AI]RIE and PR[AI]RIE-PSAI institutes (120 M€ budget, 20% from industry).
She was previously Director of the Inria Paris Center, a Board Member of Agoranov and VP of Cap Digital. She is a Board Member of the ICM Foundation and of ENPC, and a member of the GenAI Committee to the Prime Minister.
Maxim Karpushin is VP Engineering at Enhance Lab and a PhD engineer with 10+ years of experience in GPU computing, computational imaging, computer vision, and ML inference. Before Enhance Lab, he held engineering and technical leadership roles at Xiaomi, UpStride, and GoPro, with earlier experience at Thales.
He specializes in turning advanced imaging and ML research into production systems, from model and algorithm design to high-performance GPU kernels and deployment on mobile and cloud platforms. His work spans image restoration, super-resolution, video stabilization, real-time computer vision, and optimized AI inference across CUDA, OpenCL, Metal, OpenGL/GLSL, TensorRT, CoreML, and mobile ML runtimes.
A long-time GPU programming enthusiast, Maxim is passionate about extracting maximum performance from constrained hardware and co-designing algorithms, models, and compute backends for efficient real-world products.
Eva is a Principal R&D Engineer specializing in computer vision, computational photography, and image restoration. She has 10+ years of experience developing imaging algorithms, from research and prototyping to camera integration and embedded deployment. Before Enhance Lab, she spent nine years at GoPro, with earlier experience at CEA.
Her work spans multi-image super-resolution, RAW image processing, ISP, deep-learning-based restoration, embedded inference, and image quality evaluation. At Enhance Lab, she develops restoration and computational photography algorithms and works with customers to adapt and evaluate them on real-world imaging systems and constraints.
She particularly enjoys bridging imaging physics, machine learning, and practical camera constraints to turn research ideas into robust, high-quality imaging solutions.
Giuseppe is a Principal R&D Engineer with a background in machine learning, computer vision, and signal processing, with a particular interest in extracting the most useful information from different types of sensors.
Over the past 10+ years, he has worked with cameras, 360° cameras, automotive radar, lidar, and optical interferometers. His experience ranges from image restoration and computational photography to radar and lidar perception for ADAS, including sensor calibration, 3D object detection, segmentation, and tracking. He has worked at GoPro, Xiaomi, Zendar, and the National Physical Laboratory of the UK.
What he enjoys most is tackling problems at the intersection of sensors, physics, and algorithms: understanding what limits a sensing system, developing the right combination of signal processing and machine learning, and turning research ideas into solutions that work reliably on real hardware.
Long is an R&D Engineer at Enhance Lab specializing in computational imaging, super-resolution, and image restoration. He holds an MVA Master's degree and a Ph.D. in Applied Mathematics from ENS Paris-Saclay, with a focus on computer vision and computational imaging. His academic research focused on self-supervised image and video restoration for satellite imagery, leading to several CVPR publications and multiple Best Student Paper awards.
At Enhance Lab, he develops algorithms to extract maximum image quality from RAW burst data, combining registration, multi-frame fusion and super-resolution, and PSF and image-formation modeling, with a particular focus on robust reconstruction under challenging acquisition conditions.
Working closely with the production team, he focuses on turning research ideas into robust, computationally efficient solutions, co-designing algorithms and implementations for real-world camera pipelines and hardware constraints.
Yassine is an R&D Engineer at Enhance Lab, where he develops visual analysis algorithms. He holds an engineering degree from ENSAE Paris and a Master's in Data Science from Institut Polytechnique de Paris.
Before joining Enhance Lab, he worked on computer vision at Sony R&D and Inria, on 3D reconstruction, medical image segmentation, and human gesture modeling and generation.
Vincent is a PhD student at Enhance Lab and Inria, developing deep learning methods that recover sharp, high-resolution detail from degraded video. He began in medical imaging, working on MRI reconstruction and image restoration at the Université de Bordeaux, before joining Enhance Lab to focus on video. He holds an engineering degree from Télécom Paris and the MVA master's degree from ENS Paris-Saclay.
Titouan is a research engineer and is about to start a CIFRE PhD with Enhance Lab and Inria (Thoth team), working on physics-aware image restoration: combining optical modeling with learned priors so that restored images stay both convincing and faithful to reality.
He holds the MVA master's degree from ENS Paris-Saclay and an engineering degree from École des Ponts ParisTech. At Enhance Lab, where he joined as a research intern, he works on correcting lens aberrations directly in the RAW domain, from blindly estimating spatially-varying PSFs to restoring sharper, chromatically corrected images. He previously applied deep learning to medical imaging at GE Healthcare, protein modeling at Scuola Normale Superiore in Pisa, and drug design with Sanofi.
