Arnas Scopev1.1.1 Available Now
GPU deconvolution you can actually defend.
A physics-based inverse solver for 3D fluorescence microscopy. It reads your camera's noise off your own data, constrains the optics to your instrument, and gives the same answer on a laptop RTX 2060 as on an RTX 4090 — to five decimal places.
One signed installer. No CUDA Toolkit. No Python. No setup.
Built on a proven foundation
The algorithmic heritage of AutoQuant AutoDeblur — the industry standard trusted by labs worldwide — reimagined for the next decade of microscopy.
Workflow
From raw data to publication-ready results
Import your image stack
Load OME-TIFF, TIFF, CZI, LIF, ND2, and other common microscopy formats directly, including OME-Zarr.
Define your PSF
Use a measured PSF, a theoretical model, or let Arnas Scope estimate it automatically from your data.
Run deconvolution
Choose your algorithm — blind or non-blind — and let Arnas Scope do the heavy lifting.
Export and publish
Output publication-ready images and quantitative data in the formats your downstream tools expect.
Why deconvolution matters
See what your microscope was always trying to show you
Every fluorescence microscope blurs the image it captures. Light diffracts, photons scatter, and the point spread function (PSF) of your optical system smears fine structures across neighboring voxels. The result: reduced resolution, lost contrast, and quantitative errors in downstream analysis.
Deconvolution mathematically reverses this process. By modeling the PSF and applying iterative restoration algorithms, Arnas Scope recovers the true signal — sharper structures, improved axial resolution, and data you can trust for quantitative measurements.
The result is not an artistic enhancement. It is a more accurate representation of the biology you are imaging.
Capabilities
Built for serious microscopy
Multiple deconvolution algorithms
Classic Maximum Likelihood Estimation (MLE), Blind deconvolution, and more — choose the algorithm that fits your data and acquisition conditions.
All major fluorescence modalities
Widefield, confocal, spinning disk, lightsheet, TIRF, and more. Arnas Scope handles the PSF characteristics of each modality correctly.
3D volume processing
Full 3D z-stack deconvolution with proper axial PSF modeling. Recover resolution in all three dimensions, not just laterally.
Quantitative accuracy
Intensity values are preserved and corrected — not just sharpened visually. Results you can trust for downstream quantitative analysis.
Why ArnasScope
Five things nobody else in deconvolution offers
The same recipe gives the same answer on any NVIDIA card.
A figure produced on your lab's RTX 2060 and re-derived by a reviewer on an RTX 4090 has to be the same figure. We verified it: identical recipe, two GPU architectures four years apart, agreement to 3D SSIM 0.99999769 and NRMSE 0.006%. The only differences are isolated floating-point-rounding voxels. Nobody else in this field publishes this claim.
The mathematical rigor is machine-checked.
The algorithms are driven by exact, physically grounded mathematics rather than ad-hoc heuristics. The total-variation gradient identity, the GAT-FISTA step-size and Lipschitz bound, and the SMLM Cramér–Rao reparametrization invariance are proven in Lean 4 against mathlib, with zero unproven steps. Not unit-tested — proven, in the sense a mathematician means it.
It reads your camera instead of asking you to guess.
Most deconvolution software asks you for a signal-to-noise estimate. Most people guess, and the guess quietly determines the result. Arnas Scope fits the photon-transfer relation directly from your image and recovers the camera's actual gain and read noise. On our reference data it returned gain 1.201 / read 8.401 against a ground truth of ~1.2 / ~9 — without being told either.
It handles 100+ GB lightsheet datasets without crashing your RAM.
Modern lightsheet microscopes generate individual datasets that can reach tens or hundreds of gigabytes, crippling standard analysis tools. Instead of locking up when faced with a 50 GB TIFF, the pipeline natively chunks and writes volumetric data to Zarr — a cloud-optimized N-dimensional array format — permitting plane-by-plane streaming without ever exceeding host RAM.
It installs.
One signed Windows installer. CUDA 12.x bundled, Python runtime embedded, DigiCert EV code-signing. You need an NVIDIA driver (≥ 566.36) and nothing else — no CUDA Toolkit, no Anaconda, no environment, no admin-rights argument with IT. Verified end-to-end on a clean machine with only a driver installed. Includes offline activation with hardware fingerprinting, for air-gapped facilities.
Methodology
Physics, not a neural network
Learned restoration models are getting very good at producing images that look right. The open problem — now well documented in the literature — is hallucination: a network can synthesize structure because its training data contained something similar, and you cannot tell by looking at the output.
ArnasScope solves the inverse problem your optics actually posed. It can over-sharpen, it can ring, it can fail to converge — and every one of those failures is visible, diagnosable, and yours to control. It cannot make something up. If your result is going into a figure, a measurement, or a regulatory submission, that distinction is the entire argument.
PSF modeling
It knows what microscope you used
The point-spread function is constrained by your instrument, not fitted in a vacuum:
- Gibson–Lanni with a refractive-index layer stack, for depth-dependent spherical aberration
- Born–Wolf scalar widefield
- Point-scanning confocal and spinning-disk models
- A hard incoherent bandwidth limit at 2·NA/λ, so the solver cannot wander outside physics
When your optics are uncertain, Zernike-based estimation recovers the aberration from your own data — on a real bead it found the refractive-index-mismatch spherical series directly, without being told the mismatch existed.
Algorithm library
Method breadth, with receipts
Twenty-three methods, eleven running natively in signed CUDA: the full Richardson–Lucy family with five acceleration modes, seven regularizers (total-variation, Hessian-Frobenius, Huber, Geman–McClure, Tikhonov, fractional-Poisson), three background models, FISTA with wavelet sparsity, the classical direct inverse filters, variance-stabilized Richardson–Lucy, and plug-and-play BM4D.
Every method carries its primary literature citation with a DOI. Your methods section writes itself, and your reviewer can check our work.
Transparency
What we don't claim
We don't call this super-resolution.
ArnasScope recovers spatial-frequency content that your optics attenuated. That is regularized, SNR-limited inference — not new physical information. SIM, STED, and SMLM acquire information that we do not. We won't blur that line, and you should be suspicious of anyone who does.
Use a measured PSF when you have one.
Our own audits show non-blind deconvolution with a measured PSF beating every blind variant we've built, by a wide margin. We ship blind estimation to get you started when your optics are uncertain — not as a substitute for knowing them.
Uncertainty quantification is on the roadmap, not in the product.
Our Bayesian Poisson–Gaussian framework is built and numerically validated in our research library, but is not yet wired into the shipping application. When it is, we'll say so.
We haven't benchmarked the photon-starved regime yet.
We model Poisson–Gaussian noise properly and calibrate it from your data. We have not published a low-light SNR sweep, so we are not going to tell you we're the most noise-robust option available.
The scientist behind the software
Nathan J. O'Connor, PhD
Nathan J O'Connor is one of the original creators of AutoQuant AutoDeblur — the deconvolution software that became the benchmark for 3D fluorescence microscopy image restoration across academic and commercial research labs worldwide.
With decades of experience at the intersection of computational optics, image processing, and biological imaging, Nate brings unmatched depth to Arnas Scope. Every algorithm, every parameter, every design decision reflects hard-won expertise from real microscopy workflows.
Arnas Scope is not a port or a wrapper. It is a ground-up reimagining of what deconvolution software can be — built by the person who helped define the field.
Designed for the lab
Software that respects your science
Arnas Scope is designed by a microscopist, for microscopists. No black-box AI filters. No hidden processing. Every step is transparent, reproducible, and grounded in the physics of light.
Available as
- Standalone Windows GUI — point-and-click interface for interactive use
- Command-line interface (CLI) — scriptable and pipeline-friendly
- SDK with DLL — integrate deconvolution directly into your own software
Built for rigor
- Reproducible results with full parameter logging
- Batch processing for high-throughput pipelines
- Designed to meet journal image integrity standards
Common questions
Frequently asked questions
Early access
Be among the first to use Arnas Scope
We are working with a select group of labs during early access. Tell us about your imaging work and we will be in touch.
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