85 papers
This paper introduces a new framework of psychophysical tests based on low-level vision principles—specifically contrast sensitivity, masking, and matching—to evaluate and reveal the perceptual strengths and weaknesses of 34 existing image and video quality metrics, demonstrating that standard evaluation protocols often fail to capture these fundamental human visual properties.
ECLARE is an open-source, self-supervised super-resolution method that enhances anisotropic 2D MR volumes by estimating slice profiles and learning in-plane mappings without external data, thereby overcoming domain shift and outperforming existing techniques in both signal recovery and downstream tasks.
This paper proposes a neural video compression method for static scenes that incorporates positive-incentive noise to disentangle transient variations from persistent backgrounds, achieving a 73% bitrate reduction while maintaining pixel-level fidelity and avoiding hallucinated details.
This paper proposes a unified, single-stage architectural framework that jointly processes image and Stokes domains to achieve state-of-the-art performance in recovering polarimetric parameters from various degraded observations, including low-light noise, motion blur, and mosaicing artifacts, while ensuring physical consistency and avoiding error accumulation.
Uni-LVC is a unified learned video compression framework that integrates intra and inter coding into a single model by conditioning inter-coding on temporal cues via a cross-attention module and a reliability-aware classifier, thereby achieving superior rate-distortion performance across low-delay and random-access scenarios while maintaining computational efficiency.
This paper proposes a lightweight, interpretable framework that extends the Discriminative Histogram of Gradient Magnitude to 3D space, combining slice-level and volume-level features with a minimal-parameter classifier to achieve robust and accurate detection of motion artifacts in structural brain MRI across diverse acquisition sites.
This paper proposes a cardiac cine MRI reconstruction method using Gabor primitives, which combine Gaussian envelopes with complex exponentials to enable flexible k-space coverage and a low-rank spatiotemporal decomposition, achieving superior performance over compressed sensing, Gaussian primitives, and implicit neural representations while offering physically interpretable parameters.
This paper introduces PPCMI-SF, a privacy-preserving collaborative framework that utilizes client-specific latent transforms and server-side mapping to achieve high-accuracy, real-time medical image segmentation across heterogeneous institutions while effectively resisting inversion and membership inference attacks without sharing raw data.
This paper introduces OccNL, the first benchmark for 3D semantic occupancy prediction under label noise, and proposes DPR-Occ, a novel framework that leverages dual-source partial label reasoning to achieve robust performance and prevent catastrophic collapse in noisy 3D voxel spaces where existing 2D noise-robust strategies fail.
NOVA introduces a novel 3D multi-object tracking framework that leverages large language models to reformulate tracking as an autoregressive next-step sequence completion task, enabling superior open-vocabulary generalization and identity consistency through spatio-temporal semantic modeling without relying on closed-set assumptions.
This paper introduces SKETCHGAIT, a robust gait recognition framework that leverages a novel label-free "SKETCH" modality to extract dense structural cues from RGB images, demonstrating that combining this edge-based representation with traditional parsing methods significantly outperforms existing silhouette- and parsing-based approaches.
This paper introduces Multivariate Fields of Experts, a new image prior framework that generalizes existing methods using multivariate potential functions to achieve fast, interpretable, and theoretically guaranteed convergence in various inverse problems, outperforming univariate models while approaching deep learning performance with significantly fewer parameters and data.
This paper proposes a self-supervised, lightweight deep learning method that leverages self-consistency learning and inter-component correlations to achieve high-quality reconstruction of irregularly acquired seismic data without requiring external training datasets.
This paper proposes a novel multimodal framework, the Clinical-Injection Transformer with a domain-adapted MAE, which integrates routine PAS-stained histopathology images and clinical data to achieve high-accuracy three-class prognosis prediction for pediatric lupus nephritis, addressing previous limitations in data availability and modality integration.
The paper introduces AIRT, an end-to-end deep-learning framework that generates high-quality, deliverable single-arc VMAT prostate treatment plans in under one second directly from CT images and contours, demonstrating non-inferiority to standard clinical planning systems while significantly accelerating workflow efficiency.
This technical report details a collaboration with industry leaders in Pakistan's Sialkot surgical cluster to develop an Automated Optical Inspection system using deep learning models (YOLOv8, ResNet-152, and EfficientNet-b4) on a new dataset of 4,414 images to detect manufacturing defects in surgical instruments, thereby enhancing patient safety and manufacturing quality.
This paper introduces a novel pseudo-3D longitudinal inpainting framework based on Denoising Diffusion Probabilistic Models and Region-Aware Diffusion that significantly outperforms state-of-the-art baselines in perceptual fidelity, temporal stability, and processing speed for removing evolving lesions from brain MRI scans.
This paper proposes VeilGen, an unsupervised generative model that learns latent transmission and glare maps to synthesize realistic veiling glare datasets, and DeVeiler, a restoration network that leverages these maps to effectively remove veiling glare from simplified optical systems.
This study presents a novel computer vision framework that utilizes external observation techniques, including YOLO-based object detection and lane monitoring, to classify distracted and impaired driver behaviors in non-connected vehicles without relying on inter-vehicular communication.
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.