1.4k citations · 2.1k across the 85 of their papers we have counts for
93 papers
You've Seen Enough: Quality-Constrained Image Coding for Machines
Khoa Pham-Dinh, Sanaz Nami, Hamed Rezazadegan Tavakoli +2
Visual data is increasingly consumed by machine-vision systems rather than by human observers. Image Coding for Machines (ICM) compresses images assuming the main observer is a com…
Multi-Pass, Multi-View Blended Learning for High-Fidelity Volumetric CT Synthesis from Chest X-Rays
Ozer Can Devecioglu, Serkan Kiranyaz, Rashid Mazhar +3
Reconstructing volumetric Computed Tomography (CT) from a single 2D chest radiograph (CXR) is an ill-posed inverse problem, further complicated by the scarcity of paired CXR-CT tra…
From Reports to Ontologies: Ontology-Guided Representation Learning for 12-Lead ECG
Lei Xu, Fahad Sohrab, Mehmet Yamac +2
The 12-lead electrocardiogram (ECG) is a quasi-periodic, multi-channel signal with diagnostic content spanning timescales from millisecond waveform morphology to multi-second rhyth…
Axiomatizing Neural Networks via Pursuit of Subspaces
Mehmet Yamac, Mert Duman, Ugur Akpinar +4
While deep neural networks have achieved remarkable success across a wide range of domains, their underlying mechanisms remain poorly understood, and they are often regarded as bla…
The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report
Bin Ren, Hang Guo, Yan Shu +60
This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a…
MTS-CSNet: Multiscale Tensor Factorization for Deep Compressive Sensing on RGB Images
Mehmet Yamac, Lei Xu, Serkan Kiranyaz +1
Deep learning based compressive sensing (CS) methods typically learn sampling operators using convolutional or block wise fully connected layers, which limit receptive fields and s…
Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference
Li Yu, Yingbo Zhao, Shiyu Wu +3
Existing studies on quality enhancement for compressed video (QECV) predominantly rely on known quantization parameters (QPs), training separate enhancement models for each QP sett…
DRACO: Data Replication and Collection Framework for Enhanced Data Availability and Robustness in IoT Networks
Waleed Bin Qaim, Oznur Ozkasap, Rabia Qadar +1
The Internet of Things (IoT) bridges the gap between the physical and digital worlds, enabling seamless interaction with real-world objects via the Internet. However, IoT systems f…
Randomized PCA Forest for Unsupervised Outlier Detection
Muhammad Rajabinasab, Farhad Pakdaman, Moncef Gabbouj +2
We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated by the performance of Randomized PCA (RPCA) Forest in app…
UL-DD: A Multimodal Drowsiness Dataset Using Video, Biometric Signals, and Behavioral Data
Morteza Bodaghi, Majid Hosseini, Raju Gottumukkala +3
In this study, we present a comprehensive public dataset for driver drowsiness detection, integrating multimodal signals of facial, behavioral, and biometric indicators. Our datase…
Expert Operational GANS: Towards Real-Color Underwater Image Restoration
Ozer Can Devecioglu, Serkan Kiranyaz, Mehmet Yamac +1
The wide range of deformation artifacts that arise from complex light propagation, scattering, and depth-dependent attenuation makes the underwater image restoration to remain a ch…
Crucial-Diff: A Unified Diffusion Model for Crucial Image and Annotation Synthesis in Data-scarce Scenarios
Siyue Yao, Mingjie Sun, Eng Gee Lim +3
The scarcity of data in various scenarios, such as medical, industry and autonomous driving, leads to model overfitting and dataset imbalance, thus hindering effective detection an…
FANeRV: Frequency Separation and Augmentation based Neural Representation for Video
Li Yu, Zhihui Li, Chao Yao +2
Neural representations for video (NeRV) have gained considerable attention for their strong performance across various video tasks. However, existing NeRV methods often struggle to…
Multi-Scale Tensorial Summation and Dimensional Reduction Guided Neural Network for Edge Detection
Lei Xu, Mehmet Yamac, Mete Ahishali +1
Edge detection has attracted considerable attention thanks to its exceptional ability to enhance performance in downstream computer vision tasks. In recent years, various deep lear…
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction
Li Yu, Xuanzhe Sun, Wei Zhou +1
Video saliency prediction is crucial for downstream applications, such as video compression and human-computer interaction. With the flourishing of multimodal learning, researchers…
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment
Li Yu, Situo Wang, Wei Zhou +1
Inspired by the dual-stream theory of the human visual system (HVS) - where the ventral stream is responsible for object recognition and detail analysis, while the dorsal stream fo…
Multiscale Tensor Summation Factorization as a New Neural Network Layer (MTS Layer) for Multidimensional Data Processing
Mehmet Yamaç, Muhammad Numan Yousaf, Serkan Kiranyaz +1
Multilayer perceptrons (MLP), or fully connected artificial neural networks, are known for performing vector-matrix multiplications using learnable weight matrices; however, their…
Progressive Transfer Learning for Multi-Pass Fundus Image Restoration
Uyen Phan, Ozer Can Devecioglu, Serkan Kiranyaz +1
Diabetic retinopathy is a leading cause of vision impairment, making its early diagnosis through fundus imaging critical for effective treatment planning. However, the presence of…
To Vaccinate or not to Vaccinate? Analyzing Power over the Pandemic
Tanveer Khan, Fahad Sohrab, Antonis Michalas +1
The COVID-19 pandemic has profoundly affected the normal course of life -- from lock-downs and virtual meetings to the unprecedentedly swift creation of vaccines. To halt the COVID…
Dual-Domain Fusion for Semi-Supervised Learning
Tuomas Jalonen, Mohammad Al-Sa'd, Serkan Kiranyaz +1
Labeled time-series data is often expensive and difficult to obtain, making it challenging to train accurate machine learning models for real-world applications such as anomaly det…
Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach
Thomas Debelle, Fahad Sohrab, Pekka Abrahamsson +1
Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification me…
CoRe-Net: Co-Operational Regressor Network with Progressive Transfer Learning for Blind Radar Signal Restoration
Muhammad Uzair Zahid, Serkan Kiranyaz, Alper Yildirim +1
Real-world radar signals are frequently corrupted by various artifacts, including sensor noise, echoes, interference, and intentional jamming, differing in type, severity, and dura…
Deep-BrownConrady: Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data
Faiz Muhammad Chaudhry, Jarno Ralli, Jerome Leudet +4
This research addresses the challenge of camera calibration and distortion parameter prediction from a single image using deep learning models. The main contributions of this work…
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems
Aoming Liang, Qi Liu, Lei Xu +4
Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantit…