Search Results for author: Junaid Malik

Found 21 papers, 5 papers with code

2D Self-Organized ONN Model For Handwritten Text Recognition

no code implementations17 Jul 2022 Hanadi Hassen Mohammed, Junaid Malik, Somaya Al-Madeed, Serkan Kiranyaz

With their heterogeneous network structure incorporating non-linear neurons, Operational Neural Networks (ONNs) have recently been proposed to address this drawback.

Handwritten Text Recognition

Blind ECG Restoration by Operational Cycle-GANs

2 code implementations29 Jan 2022 Serkan Kiranyaz, Ozer Can Devecioglu, Turker Ince, Junaid Malik, Muhammad Chowdhury, Tahir Hamid, Rashid Mazhar, Amith Khandakar, Anas Tahir, Tawsifur Rahman, Moncef Gabbouj

Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult.

Denoising ECG Denoising

Image denoising by Super Neurons: Why go deep?

no code implementations29 Nov 2021 Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj

As the integration of non-local information is known to benefit denoising, in this work we investigate the use of super neurons for both synthetic and real-world image denoising.

Image Denoising

Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks

no code implementations30 Sep 2021 Turker Ince, Junaid Malik, Ozer Can Devecioglu, Serkan Kiranyaz, Onur Avci, Levent Eren, Moncef Gabbouj

Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs.

Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks

no code implementations30 Sep 2021 Junaid Malik, Ozer Can Devecioglu, Serkan Kiranyaz, Turker Ince, Moncef Gabbouj

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 accuracy for classifying patient-specific ECG are still few.

Arrhythmia Detection Classification +1

Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks

1 code implementation30 Sep 2021 Moncef Gabbouj, Serkan Kiranyaz, Junaid Malik, Muhammad Uzair Zahid, Turker Ince, Muhammad Chowdhury, Amith Khandakar, Anas Tahir

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 acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors.

Computational Efficiency

Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs

no code implementations28 Sep 2021 Ozer Can Devecioglu, Junaid Malik, Turker Ince, Serkan Kiranyaz, Eray Atalay, Moncef Gabbouj

Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain.

Super Neurons

no code implementations3 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.

Self-Organized Residual Blocks for Image Super-Resolution

no code implementations31 May 2021 Onur Keleş, A. Murat Tekalp, Junaid Malik, Serkan Kiranyaz

It has become a standard practice to use the convolutional networks (ConvNet) with RELU non-linearity in image restoration and super-resolution (SR).

Image Restoration Image Super-Resolution

Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression

no code implementations25 May 2021 M. Akin Yilmaz, Onur Keleş, Hilal Güven, A. Murat Tekalp, Junaid Malik, Serkan Kiranyaz

In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space.

Image Compression

BM3D vs 2-Layer ONN

no code implementations4 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.

Image Denoising

Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising

1 code implementation4 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.

Image Denoising Image Restoration

Operational vs Convolutional Neural Networks for Image Denoising

no code implementations1 Sep 2020 Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj

Convolutional Neural Networks (CNNs) have recently become a favored technique for image denoising due to its adaptive learning ability, especially with a deep configuration.

Image Denoising

Self-Organized Operational Neural Networks for Severe Image Restoration Problems

no code implementations29 Aug 2020 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.

Denoising Image Restoration

Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity

no code implementations21 Aug 2020 Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah, Turker Ince, Alexandros Iosifidis, Moncef Gabbouj

As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data.

Learning Theory

Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection

no code implementations11 Aug 2020 Serkan Kiranyaz, Aysen Degerli, Tahir Hamid, Rashid Mazhar, Rayyan Ahmed, Rayaan Abouhasera, Morteza Zabihi, Junaid Malik, Ridha Hamila, Moncef Gabbouj

It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF).

Motion Estimation Myocardial infarction detection

FastONN -- Python based open-source GPU implementation for Operational Neural Networks

1 code implementation3 Jun 2020 Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj

Operational Neural Networks (ONNs) have recently been proposed as a special class of artificial neural networks for grid structured data.

Self-Organized Operational Neural Networks with Generative Neurons

2 code implementations24 Apr 2020 Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah, Turker Ince, Alexandros Iosifidis, Moncef Gabbouj

However, Greedy Iterative Search (GIS) method, which is the search method used to find optimal operators in ONNs takes many training sessions to find a single operator set per layer.

Computational Efficiency

3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images

no code implementations9 Apr 2019 Junaid Malik, Serkan Kiranyaz, Riyadh Al-Raoush, Olivier Monga, Patricia Garnier, Sebti Foufou, Abdelaziz Bouras, Alexandros Iosifidis, Moncef Gabbouj, Philippe C. Baveye

Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales.

Clustering Image Segmentation +2

Colorectal cancer diagnosis from histology images: A comparative study

no code implementations27 Mar 2019 Junaid Malik, Serkan Kiranyaz, Suchitra Kunhoth, Turker Ince, Somaya Al-Maadeed, Ridha Hamila, Moncef Gabbouj

Moreover, we conduct quantitative comparative evaluations among the traditional methods, transfer learning-based methods and the proposed adaptive approach for the particular task of cancer detection and identification from scarce and low-resolution histology images.

Transfer Learning

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