249 citations · 1.5k across the 70 of their papers we have counts for
17 papers · 1 filter
Image denoising by Super Neurons: Why go deep?
Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
Classical image denoising methods utilize the non-local self-similarity principle to effectively recover image content from noisy images. Current state-of-the-art methods use deep…
Learning to ignore: rethinking attention in CNNs
Firas Laakom, Kateryna Chumachenko, Jenni Raitoharju +2
Recently, there has been an increasing interest in applying attention mechanisms in Convolutional Neural Networks (CNNs) to solve computer vision tasks. Most of these methods learn…
Robust channel-wise illumination estimation
Firas Laakom, Jenni Raitoharju, Jarno Nikkanen +2
Recently, Convolutional Neural Networks (CNNs) have been widely used to solve the illuminant estimation problem and have often led to state-of-the-art results. Standard approaches…
Early Myocardial Infarction Detection over Multi-view Echocardiography
Aysen Degerli, Serkan Kiranyaz, Tahir Hamid +2
Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a blockage of the coronary arteries feeding the myocardium. An early diagnosis of MI an…
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
Moncef Gabbouj, Serkan Kiranyaz, Junaid Malik +5
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acqu…
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks
Junaid Malik, Ozer Can Devecioglu, Serkan Kiranyaz +2
Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high ac…
Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs
Ozer Can Devecioglu, Junaid Malik, Turker Ince +3
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progr…
Remote Multilinear Compressive Learning with Adaptive Compression
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Multilinear Compressive Learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. The level of signal compression affects the detection…
Super Neurons
Serkan Kiranyaz, Junaid Malik, Mehmet Yamac +5
Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neuro…
Bilinear Input Normalization for Neural Networks in Financial Forecasting
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj +1
Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network. This is…
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation
Mehmet Yamac, Ugur Akpinar, Erdem Sahin +2
In CS literature, the efforts can be divided into two groups: finding a measurement matrix that preserves the compressed information at the maximum level, and finding a reconstruct…
Representation Based Regression for Object Distance Estimation
Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz +1
In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional…
Graph-Embedded Subspace Support Vector Data Description
Fahad Sohrab, Alexandros Iosifidis, Moncef Gabbouj +1
In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes…
BM3D vs 2-Layer ONN
Junaid Malik, Serkan Kiranyaz, Mehmet Yamac +1
Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs. Older but computationally more efficient met…
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising
Junaid Malik, Serkan Kiranyaz, Mehmet Yamac +2
Real-world blind denoising poses a unique image restoration challenge due to the non-deterministic nature of the underlying noise distribution. Prevalent discriminative networks tr…
Ensembling object detectors for image and video data analysis
Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We fur…
Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network
Muhammad Uzair Zahid, Serkan Kiranyaz, Turker Ince +5
Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic…