86 papers
This paper introduces ResSR, a computationally efficient, unsupervised model-based method for multispectral image super-resolution that decouples spectral and spatial processing to achieve high-quality reconstruction with significantly reduced computational cost compared to existing approaches.
This paper proposes an approximate expectation-maximization algorithm that directly reconstructs 3-D molecular structures from low signal-to-noise ratio cryo-EM micrographs, bypassing the traditional and often failing step of locating individual projection images.
This paper proposes a multi-volume latent consistency model that leverages three-dimensional latent representations from multiple fields of view to achieve fast, stable, and high-precision limited-angle CT reconstruction, effectively preserving organ structures and maintaining performance across diverse and unseen clinical imaging conditions.
This paper proposes a snapshot HDR imaging sensor that utilizes a non-regular arrangement of two differently sized prototype pixels to extend dynamic range and mitigate aliasing, enabling high-detail image reconstruction via Fourier domain processing.
This paper presents an energy-efficient Adiabatic Capacitive Neuron (ACN) implemented in 0.18μm CMOS technology that achieves over 90% synapse energy savings compared to non-adiabatic benchmarks while offering improved accuracy, robustness, and scalability through a novel low-offset Threshold Logic activation function.
This paper proposes a method for generic camera calibration using blurry images by simultaneously estimating feature locations and spatially varying point spread functions through geometric constraints and a local parametric illumination model, thereby overcoming the motion blur issues inherent in capturing numerous calibration images.
This study demonstrates that simple lung cropping preprocessing effectively mitigates racial shortcut learning in chest X-ray diagnosis models by suppressing spurious racial cues while preserving diagnostic accuracy, thereby avoiding the typical fairness-accuracy trade-off.
This paper presents a 360-degree multi-camera system for detecting blue emergency lights that utilizes a curated ABLDataset and an enhanced RT-DETR model with a color attention block to achieve high accuracy, azimuthal localization, and approach angle estimation for integration into Advanced Driver Assistance Systems.
The paper proposes CogGen, a fully unsupervised deep generative modeling framework for compressively sampled MRI reconstruction that enhances fidelity and convergence by regulating cognitive load through a self-paced curriculum learning strategy that progressively schedules k-space data fitting from low-frequency, high-SNR samples to more complex, noise-dominated measurements.
This paper proposes an explainability-guided active learning framework that improves medical image analysis by strategically selecting samples based on both predictive uncertainty and attention misalignment with expert-defined regions, thereby achieving superior data efficiency and clinical interpretability compared to traditional methods.
The paper introduces GMLN-BTS, a lightweight graph-based multi-modal network that achieves state-of-the-art brain tumor segmentation with high precision and minimal computational cost (4.58M parameters) by integrating a modality-aware encoder, a graph-based collaborative interaction module, and a voxel refinement upsampling mechanism.
Overtone is a unified framework for transformer-based PDE surrogates that employs cyclic patch size modulation via architecture-agnostic modules to dynamically distribute harmonic errors and adapt computational costs, achieving significantly lower long-term rollout errors and flexible efficiency compared to static-patch baselines.
This paper introduces MedFuncta, a unified meta-learning framework that encodes diverse medical images into compact 1D latent vectors to train shared, continuous neural fields at scale, while optimizing training efficiency through sparse supervision and a novel frequency schedule, and releases the accompanying MedNF dataset with over 500,000 latent vectors to advance large-scale medical neural field research.
This paper proposes a cost-effective DNA storage system for progressive images that leverages Nanopore adaptive sampling to enable PCR-free random access, allowing users to retrieve specific resolution layers by selectively sequencing only the necessary oligos rather than the entire pool.
The paper introduces ICHOR, a self-supervised masked autoencoder framework pretrained on a large, multi-site dataset of 11,405 ASL CBF maps, which outperforms existing methods in learning robust, transferable representations for downstream diagnostic and quality prediction tasks.
This paper introduces HoloPASWIN, a physics-aware deep learning framework based on Swin Transformers that effectively suppresses twin-image artifacts and achieves robust inline holographic reconstruction by combining hierarchical shifted-window attention with a differentiable angular spectrum propagator for physical consistency.
This paper demonstrates that diffusion-based image editing inherently compromises robust invisible watermarks by treating embedded payloads as noise to be removed during the denoising process, leading to a theoretical and empirical analysis of this fragility and proposing guidelines for future watermarking designs.
This paper introduces Persistent Homology Convolutions, a novel method that captures local topological features in histopathology slides, demonstrating that this approach outperforms standard CNNs in classification accuracy and hyperparameter robustness by effectively integrating geometric information into deep learning models.
This paper characterizes Apple's Synthetic Defocus Noise Pattern (SDNP) found in iPhone portrait-mode images, proposing a modeling method to mitigate its interference with PRNU-based camera source verification while demonstrating its utility for tracing images across different iPhone models and iOS versions.
This paper proposes the Channel-Spatial Attention Synergy Network (CSASN), a novel multitask learning framework that integrates dual-branch EfficientNet and ViT architectures with attention mechanisms to effectively address data imbalance and morphological heterogeneity for the accurate diagnosis of rare thyroid carcinoma subtypes using ultrasound imaging.