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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The Double-edged Sword of Data-driven Super-Resolution: Adversarial Super-resolution Models

Data-driven super-resolution (SR) methods are often integrated into imaging pipelines as preprocessing steps to improve downstream tasks such as classification and detection. However, these SR models introduce a previously unexplored attack surface into imaging pipelines. In this paper, we present AdvSR, a framework demonstrating that adversarial behavior can be embedded directly into SR model weights during training, requiring no access to inputs at inference time. Unlike prior attacks that perturb inputs or rely on backdoor triggers, AdvSR operates entirely at the model level. By jointly optimizing for reconstruction quality and targeted adversarial outcomes, AdvSR produces models that appear benign under standard image quality metrics while inducing downstream misclassification. We evaluate AdvSR on three SR architectures (SRCNN, EDSR, SwinIR) paired with a YOLOv11 classifier and demonstrate that AdvSR models can achieve high attack success rates with minimal quality degradation. These findings highlight a new model-level threat for imaging pipelines, with implications for how practitioners source and validate models in safety-critical applications.

Sullivan, Haley [ORNL] (ORCID:0000000274069217)↗
DOE OSTI2023

Super-resolution computational saturated absorption microscopy

Imaging beyond the diffraction limit barrier has attracted wide attention due to the ability to resolve previously hidden image features. Of the various super-resolution microscopy techniques available, a particularly simple method called saturated excitation microscopy (SAX) requires only simple modification of a laser scanning microscope: The illumination beam power is sinusoidally modulated and driven into saturation. SAX images are extracted from the harmonics of the modulation frequency and exhibit improved spatial resolution. Unfortunately, this elegant strategy is hindered by the incursion of shot noise that prevents high-resolution imaging in many realistic scenarios. Here, we demonstrate a technique for super-resolution imaging that we call computational saturated absorption (CSA) in which a joint deconvolution is applied to a set of images with diversity in spatial frequency support among the point spread functions (PSFs) used in the image formation with saturated laser scanning fluorescence microscopy. CSA microscopy allows access to the high spatial frequency diversity in a set of saturated effective PSFs, while avoiding image degradation from shot noise.

Murray, Gabe (ORCID:0000000295655485)↗
DOE OSTI2017

Spatial-Spectral Representation for X-Ray Fluorescence Image Super-Resolution

X-ray fluorescence (XRF) scanning of works of art is becoming an increasing popular nondestructive analyticalmethod. The high quality XRF spectra is necessary to obtain significant information on both major and minor elements used for characterization and provenance analysis. However, there is a tradeoff between the spatial resolution of an XRF scan and the signalto- noise ratio (SNR) of each pixel’s spectrum, due to the limited scanning time. In this project, we propose an XRF image super-resolution method to address this tradeoff; thus, obtaining a high spatial resolution XRF scan with high SNR. We fuse a lowresolution XRF image and a conventional RGB high-resolution image into a product of both high spatial and high spectral resolution XRF image. There is no guarantee of a one to one mapping between XRF spectrum and RGB color since, for instance, paintings with hidden layers cannot be detected in visible but can in X-ray wavelengths. We separate the XRF image into the visible and nonvisible components. The spatial resolution of the visible component is increased utilizing the high-resolution RGB image, whereas the spatial resolution of the non-visible component is increased using a total variation super-resolution method. Finally, the visible and nonvisible components are combined to obtain the final result.

Dai, Qiqin↗
DOE OSTI2018

Distinguishing one from many using super-resolution compressive sensing

We present that distinguishing whether a signal corresponds to a single source or a limited number of highly overlapping point spread functions (PSFs) is a ubiquitous problem across all imaging scales, whether detecting receptor-ligand interactions in cells or detecting binary stars. Super-resolution imaging based upon compressed sensing exploits the relative sparseness of the point sources to successfully resolve sources which may be separated by much less than the Rayleigh criterion. However, as a solution to an underdetermined system of linear equations, compressive sensing requires the imposition of constraints which may not always be valid. One typical constraint is that the PSF is known. However, the PSF of the actual optical system may reflect aberrations not present in the theoretical ideal optical system. Even when the optics are well characterized, the actual PSF may reflect factors such as non-uniform emission of the point source (e.g. fluorophore dipole emission). As such, the actual PSF may differ from the PSF used as a constraint. Similarly, multiple different regularization constraints have been suggested including the l 1 -norm, l 0 -norm, and generalized Gaussian Markov random fields (GGMRFs), each of which imposes a different constraint. Other important factors include the signal-to-noise ratio of the point sources and whether the point sources vary in intensity. In this work, we explore how these factors influence super-resolution image recovery robustness, determining the sensitivity and specificity. In conclusion, we determine an approach that is more robust to the types of PSF errors present in actual optical systems.

42 ENGINEERING↗
DOE OSTI2015

Super-resolution and super-localization microscopy: A novel tool for imaging chemical and biological processes

Optical microscopy imaging of single molecules and single particles is an essential method for studying fundamental biological and chemical processes at the molecular and nanometer scale. The best spatial resolution (~ λ/2) achievable in traditional optical microscopy is governed by the diffraction of light. However, single molecule-based super-localization and super-resolution microscopy imaging techniques have emerged in the past decade. Individual molecules can be localized with nanometer scale accuracy and precision for studying of biological and chemical processes.This work uncovered the heterogeneous properties of the pore structures. In this dissertation, the coupling of molecular transport and catalytic reaction at the single molecule and single particle level in multilayer mesoporous nanocatalysts was elucidated. Most previous studies dealt with these two important phenomena separately. A fluorogenic oxidation reaction of non-fluorescent amplex red to highly fluorescent resorufin was tested. The diffusion behavior of single resorufin molecules in aligned nanopores was studied using total internal reflection fluorescence microscopy (TIRFM).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deploying Adversarial Attacks in Super-Resolution Models

Reliable super-resolution methods are crucial for applications like remote sensing, grid resilience and disaster impact analysis, and standoff biometrics. These methods infuse additional high-frequency information into reconstructions, allowing for better contextualization and image intelligence. However, super-resolution models can also introduce hallucinations or other unseen vulnerabilities that could be exploited by an adversary. This is further compounded by the prominence of deep learning in these models, as models are often blindly applied on out-of-distribution images. In this work, we implement adversarial attacks in common open-source super-resolution models and examine their impact on reconstructions and downstream classification tasks. We find that an adversarially trained super-resolution model can produce high-quality reconstructions that degrade downstream classifications. Moreover, these attacks do not require access to low-resolution imagery or class labels at inference time. These results demonstrate the vulnerability of super-resolution methods to malicious actors and motivates the development of a detector for super-resolution adversarial attacks. Further exploration of adversarial attacks in this domain is required to ensure trustworthiness and robustness of super-resolution models for national security applications.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2013

Oblique reconstructions in tomosynthesis. II. Super-resolution

Purpose: In tomosynthesis, super-resolution has been demonstrated using reconstruction planes parallel to the detector. Super-resolution allows for subpixel resolution relative to the detector. The purpose of this work is to develop an analytical model that generalizes super-resolution to oblique reconstruction planes.Methods: In a digital tomosynthesis system, a sinusoidal test object is modeled along oblique angles (i.e., “pitches”) relative to the plane of the detector in a 3D divergent-beam acquisition geometry. To investigate the potential for super-resolution, the input frequency is specified to be greater than the alias frequency of the detector. Reconstructions are evaluated in an oblique plane along the extent of the object using simple backprojection (SBP) and filtered backprojection (FBP). By comparing the amplitude of the reconstruction against the attenuation coefficient of the object at various frequencies, the modulation transfer function (MTF) is calculated to determine whether modulation is within detectable limits for super-resolution. For experimental validation of super-resolution, a goniometry stand was used to orient a bar pattern phantom along various pitches relative to the breast support in a commercial digital breast tomosynthesis system.Results: Using theoretical modeling, it is shown that a single projection image cannot resolve a sine input whose frequency exceeds the detector alias frequency. The high frequency input is correctly visualized in SBP or FBP reconstruction using a slice along the pitch of the object. The Fourier transform of this reconstructed slice is maximized at the input frequency as proof that the object is resolved. Consistent with the theoretical results, experimental images of a bar pattern phantom showed super-resolution in oblique reconstructions. At various pitches, the highest frequency with detectable modulation was determined by visual inspection of the bar patterns. The dependency of the highest detectable frequency on pitch followed the same trend as the analytical model. It was demonstrated that super-resolution is not achievable if the pitch of the object approaches 90°, corresponding to the case in which the test frequency is perpendicular to the breast support. Only low frequency objects are detectable at pitches close to 90°.Conclusions: This work provides a platform for investigating super-resolution in oblique reconstructions for tomosynthesis. In breast imaging, this study should have applications in visualizing microcalcifications and other subtle signs of cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Advances in Image-Domain Multi-Resolution and Super-Resolution Algorithms for Industrial X-Ray Computed Tomography: A Literature Survey and New Insights

Industrial X-ray Computed Tomography (XCT) is a nondestructive method for inspection and character ization of materials and parts. XCT captures images of a part from various angles, and these images are then used to construct Three-Dimensional (3D) representations of that part. This method enables assessing the quality of the parts, identifying defects and understanding their physical properties without damaging them.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Operating organic light-emitting diodes imaged by super-resolution spectroscopy

Super-resolution stimulated emission depletion microscopy is adapted here for materials characterization that would not otherwise be possible. With the example of organic lightemitting diodes (OLEDs), spectral imaging with pixel-by-pixel wavelength discrimination allows us to resolve local-chain environment encoded in the spectral response of the semiconducting polymer, and correlate chain packing with local electroluminescence by using externally applied current as the excitation source. We observe nanoscopic defects that would be unresolvable by traditional microscopy. They are revealed in electroluminescence maps in operating OLEDs with 50 nm spatial resolution. We find that brightest emission comes from regions with more densely packed chains. Conventional microscopy of an operating OLED would lack the resolution needed to discriminate these features, while traditional methods to resolve nanoscale features generally cannot be performed when the device is operating. Furthermore, this points the way towards real-time analysis of materials design principles in devices as they actually operate.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Ultrafast CMOS image sensors and data-enabled super-resolution for multimodal radiographic imaging and tomography

We summarize recent progress in ultrafast Complementary Metal Oxide Semiconductor (CMOS) image sensor development and the application of neural networks for post-processing of CMOS and charge-coupled device (CCD) image data to achieve sub-pixel resolution (thus $super$-$resolution$). The combination of novel CMOS pixel designs and data-enabled image post-processing provides a promising path towards ultrafast high-resolution multi-modal radiographic imaging and tomography applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Reconstructing High Resolution ESM Data Through a Novel Fast Super Resolution Convolutional Neural Network (FSRCNN)

In this work, we present the first application of a fast super resolution convolutional neural network (FSRCNN) based approach for downscaling earth system model (ESM) simulations. Unlike other SR approaches, FSRCNN uses the same input feature dimensions as the low resolution input. This allows it to have smaller convolution layers, avoiding over-smoothing, and reducing computational costs. We adapt the FSRCNN to improve reconstruction on ESM data, we term the FSRCNN-ESM. We use high-resolution (~0.25°) monthly averaged model output of five surface variables over North America from the US Department of Energy's Energy Exascale Earth System Model's control simulation. These high-resolution and corresponding coarsened low-resolution (~1°) pairs of images are used to train the FSRCNN-ESM and evaluate its use as a downscaling approach. We find that FSRCNN-ESM outperforms FSRCNN and other super-resolution methods in reconstructing high resolution images producing finer spatial scale features with better accuracy for surface temperature, surface radiative fluxes, and precipitation.

58 GEOSCIENCES↗
DOE OSTI2014

Fabrication of optical multilayer for two-color phase plate in super-resolution microscope

In super-resolution microscopy based on fluorescence depletion, the two-color phase plate (TPP) is an indispensable optical element, which can independently control the phase shifts for two beams of different color, i.e., the pump and erase beams. By controlling a phase shift of the erase beam through the TPP, the erase beam can be modulated into a doughnut shape, while the pump beam maintains the initial Gaussian shape. To obtain a reliable optical multiplayer (ML) for the TPP, we designed a ML with only two optical layers by performing numerical optimization. The measured phase shifts generated by the fabricated ML using interferometry correspond to the design values. The beam profiles in the focal plane are also consistent with theoretical results. Although the fabricated ML consists of only two optical layers, the ML can provide a suitable phase modulation function for the TPP in a practical super-resolution microscope.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

The internal architecture of dendritic spines revealed by super-resolution imaging: What did we learn so far?

The molecular architecture of dendritic spines defines the efficiency of signal transmission across excitatory synapses. It is therefore critical to understand the mechanisms that control the dynamic localization of the molecular constituents within spines. However, because of the small scale at which most processes within spines take place, conventional light microscopy techniques are not adequate to provide the necessary level of resolution. Recently, super-resolution imaging techniques have overcome the classical barrier imposed by the diffraction of light, and can now resolve the localization and dynamic behavior of proteins within small compartments with nanometer precision, revolutionizing the study of dendritic spine architecture. Here, we highlight exciting new findings from recent super-resolution studies on neuronal spines, and discuss how these studies revealed important new insights into how protein complexes are assembled and how their dynamic behavior shapes the efficiency of synaptic transmission.

60 APPLIED LIFE SCIENCES↗