1 code implementation • 12 Jul 2023 • Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz, Moncef Gabbouj
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task.
no code implementations • 1 Jun 2023 • Dan Yang, Mehmet Yamac
It is also important to note that recent modern cameras (e. g., cameras in mobile phones) dynamically set the exposure time of the image, which presents an additional problem for networks developed for a fixed number of event frames.
Ranked #2 on Deblurring on GoPro (using extra training data)
1 code implementation • 29 Mar 2023 • Alexander Ulrichsen, Paul Murray, Stephen Marshall, Moncef Gabbouj, Serkan Kiranyaz, Mehmet Yamac, Nour Aburaed
This work focuses on extending the convolutional filters of a popular super-resolution model to more powerful operational filters to enhance the model performance on hyperspectral images.
no code implementations • 4 Aug 2021 • Mehmet Yamac, Ugur Akpinar, Erdem Sahin, Serkan Kiranyaz, Moncef Gabbouj
For a special case where the CS operation is set as a single tensor multiplication, the model is reduced to the learning-based separable CS; while a dense CS matrix can be approximated and learned as the summation of multiple tensors.
no code implementations • 3 Aug 2021 • Serkan Kiranyaz, Junaid Malik, Mehmet Yamac, Mert Duman, Ilke Adalioglu, Esin Guldogan, Turker Ince, Moncef Gabbouj
In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection.
2 code implementations • 27 Jun 2021 • Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz, Moncef Gabbouj
To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR).
1 code implementation • 4 Mar 2021 • Junaid Malik, Serkan Kiranyaz, Mehmet Yamac, Esin Guldogan, Moncef Gabbouj
Real-world blind denoising poses a unique image restoration challenge due to the non-deterministic nature of the underlying noise distribution.
no code implementations • 4 Mar 2021 • Junaid Malik, Serkan Kiranyaz, Mehmet Yamac, Moncef Gabbouj
Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs.
no code implementations • 26 Sep 2020 • Aysen Degerli, Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Khalid Hameed, Tahir Hamid, Rashid Mazhar, Moncef Gabbouj
To accomplish this, we have compiled the largest dataset with 119, 316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach.
no code implementations • Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2020 • Dan Yang, Mehmet Yamac
As part of the network, event data is first used by the high blur region segmentation module that creates a probability-like score for areas exhibiting high relative motion to the camera.
Ranked #4 on Image Deblurring on GoPro (using extra training data)
1 code implementation • 7 Jun 2020 • Mete Ahishali, Aysen Degerli, Mehmet Yamac, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Khalid Hameed, Tahir Hamid, Rashid Mazhar, Moncef Gabbouj
The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage.
no code implementations • 8 May 2020 • Mehmet Yamac, Mete Ahishali, Aysen Degerli, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Moncef Gabbouj
Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance.
no code implementations • 2 Mar 2020 • Mehmet Yamac, Mete Ahishali, Serkan Kiranyaz, Moncef Gabbouj
Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e. g., Compressively Sensed Measurements).
no code implementations • 20 Jun 2019 • Mehmet Yamac, Mete Ahishali, Nikolaos Passalis, Jenni Raitoharju, Bulent Sankur, Moncef Gabbouj
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning.
2 code implementations • 17 May 2019 • Dat Thanh Tran, Mehmet Yamac, Aysen Degerli, Moncef Gabbouj, Alexandros Iosifidis
Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of measurements.
no code implementations • 15 Oct 2018 • Aysen Degerli, Sinem Aslan, Mehmet Yamac, Bulent Sankur, Moncef Gabbouj
Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal.