Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2105.06533

Multi-Resolution Data Fusion for Super Resolution Imaging

Emma J Reid·Lawrence F Drummy·Charles A Bouman·Gregery T Buzzard
View original source ↗Read source document ↗Export metadata ↓

Abstract

Applications in materials and biological imaging are limited by the ability to collect high-resolution data over large areas in practical amounts of time. One solution to this problem is to collect low-resolution data and interpolate to produce a high-resolution image. However, most existing super-resolution algorithms are designed for natural images, often require aligned pairing of high and low-resolution training data, and may not directly incorporate a model of the imaging sensor. In this paper, we present a Multi-resolution Data Fusion (MDF) algorithm for accurate interpolation of low-resolution data at multiple resolutions up to 8x. Our approach uses small quantities of unpaired high-resolution data to train a neural network prior model denoiser and then uses the Multi-Agent Consensus Equilibrium (MACE) problem formulation to balance this denoiser with a forward model agent that promotes fidelity to measured data. A key theoretical novelty is the analysis of mismatched back-projectors, which modify typical forward model updates for computational efficiency or improved image quality. We use MACE to prove that using a mismatched back-projector is equivalent to using a standard back-projector and an appropriately modified prior model. We present electron microscopy results at 4x and 8x interpolation factors that exhibit reduced artifacts relative to existing methods while maintaining fidelity to acquired data and accurately resolving sub-pixel-scale features.

Explore related subjects

eess.IV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Emma J Reid, Lawrence F Drummy, Charles A Bouman, Gregery T Buzzard. 2022-01-01. Multi-Resolution Data Fusion for Super Resolution Imaging. https://doi.org/10.1109/tci.2022.3140551

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications

Hyperspectral imaging is a powerful bioimaging tool which can uncover novel insights, thanks to its sensitivity to the intrinsic properties of materials. However, this enhanced contrast comes at the cost of system complexity, constrained by an inherent trade-off between spatial, spectral, and temporal resolution. To overcome this limitation, we present a self-supervised deep learning-based approach that restores and enhances pixel resolution post-acquisition without requiring external training data beyond the images to be restored. Fine-tuned using metrics aligned with the imaging model, our physics-aware method achieves a 16$\times$ pixel super-resolution enhancement and a 12$\times$ imaging speedup without the need of additional training data for transfer learning. Applied to both synthetic and experimental data from five different sample types, including healthy and diseased tissues, we demonstrate that the model preserves biological integrity, as we did not detect systematic loss of biological features or biologically consequential hallucinations in tested datasets. We also concretely demonstrate the model's ability to reveal disease-associated metabolic changes that would otherwise remain undetectable. Furthermore, we provide physical insights into the model's inner workings, paving the way for future refinements that could potentially reveal novel high resolution features in an explainable manner. All methods are available as open-source software on GitHub.

eess.IV↗
arXiv2026

OASIS: Online Adaptive Video Compression via Closed-loop Feedback Control

Computer vision systems are a key building block in an autonomous vehicle, responsible for a range of perception tasks. However, they incur massive data transmission over long communication links from multiple cameras, creating a critical bandwidth and energy bottleneck. Although conventional codecs such as H.264 can reduce data rates, they are ill-suited for real-time vision systems due to high processing latency and energy consumption, as well as their reliance on static user-defined compression settings. In light of these challenges, we propose OASIS, an adaptive video compression framework that integrates lightweight in-sensor compression with task-aware compression ratio control. Based on the real-time task performance, it dynamically updates the optimal compression ratio. Experimental results demonstrate that OASIS generalizes across multiple vision tasks, achieving on average a 6x data compression, 5.8x reduction in link power consumption, and 2.5x reduction in link latency, with at most 1.5% performance degradation.

eess.IV↗
arXiv2026

Echo-E$^3$Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation

Left ventricular ejection fraction (LVEF) is a primary marker of cardiac function. However, routine estimation from endocardial measurements requires manual delineation at end-diastole (ED) and end-systole (ES), a process that is time-consuming and subject to inter-observer variability. Reliable automation is especially valuable for point-of-care ultrasound (POCUS), where computational resources are limited and acquisition quality varies. We propose Echo-E$^3$Net, an anatomy-guided spatio-temporal network that explicitly embeds cardiac anatomy into LVEF prediction. A dual-phase Endocardial Border Detector (E$^2$CBD) uses phase-specific cross-attention to localize ED/ES endocardial landmarks and produce phase-aware landmark embeddings, while an Endocardial Feature Aggregator (E$^2$FA) fuses these embeddings with global statistical descriptors of deep feature maps to refine EF regression. Training is guided by a lightweight geometric loss that uses ED and ES endocardial landmarks to regularize EF prediction. On EchoNet-Dynamic and a PSAX subset of EchoNet-Pediatric, Echo-E$^3$Net attains competitive performance using only 1.55M parameters and 8.05 GFLOPs, an order-of-magnitude compute reduction versus recent baselines, supporting real-time deployment. Our code is publicly available at https://github.com/moeinheidari7829/Echo-E3Net.

eess.IV↗