Browsing by Author "Malik, Junaid"

Now showing items 1-6 of 6

    • 2D Self-organized ONN model for Handwritten Text Recognition 

      Mohammed, Hanadi Hassen; Malik, Junaid; Al-Maadeed, Somaya; Kiranyaz, Serkan ( Elsevier , 2022 , Article)
      Deep Convolutional Neural Networks (CNNs) have recently reached state-of-the-art Handwritten Text Recognition (HTR) performance. However, recent research has shown that typical CNNs' learning performance is limited since ...

    • Blind ECG Restoration by Operational Cycle-GANs 

      Kiranyaz, Serkan; Devecioglu, Ozer Can; Ince, Turker; Malik, Junaid; Chowdhury, Muhammad; ... more authors ( IEEE Computer Society , 2022 , Article)
      Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies ...

    • BM3D VS 2-LAYER ONN 

      Malik, Junaid; Kiranyaz, Serkan; Yamac, Mehmet; Gabbouj, Moncef ( IEEE Computer Society , 2021 , Conference Paper)
      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 methods such as BM3D remain a popular choice, ...

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

      Gabbouj, Moncef; Kiranyaz, Serkan; Malik, Junaid; Zahid, Muhammad Uzair; Ince, Turker; ... more authors ( Institute of Electrical and Electronics Engineers Inc. , 2022 , Article)
      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) ...

    • SELF-ORGANIZED RESIDUAL BLOCKS FOR IMAGE SUPER-RESOLUTION 

      Keleş, Onur; Tekalp, A. Murat; Malik, Junaid; Kιranyaz, Serkan ( IEEE Computer Society , 2021 , Conference Paper)
      It has become a standard practice to use the convolutional networks (ConvNet) with RELU non-linearity in image restoration and super-resolution (SR). Although the universal approximation theorem states that a multi-layer ...

    • SELF-ORGANIZED VARIATIONAL AUTOENCODERS (SELF-VAE) FOR LEARNED IMAGE COMPRESSION 

      Yílmaz, M. Akín; Kelesş, Onur; Güven, Hilal; Tekalp, A. Murat; Malik, Junaid; ... more authors ( IEEE Computer Society , 2021 , Conference Paper)
      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. Recently, ...