24 citations · 105 across the 22 of their papers we have counts for
16 papers · 1 filter
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…
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…
Performance Indicator in Multilinear Compressive Learning
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Recently, the Multilinear Compressive Learning (MCL) framework was proposed to efficiently optimize the sensing and learning steps when working with multidimensional signals, i.e.…
Self-Organized Operational Neural Networks for Severe Image Restoration Problems
Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs. It has become…