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MTS-CSNet: Multiscale Tensor Factorization for Deep Compressive Sensing on RGB Images
Mehmet Yamac, Lei Xu, Serkan Kiranyaz +1
Deep learning based compressive sensing (CS) methods typically learn sampling operators using convolutional or block wise fully connected layers, which limit receptive fields and s…
Progressive Transfer Learning for Multi-Pass Fundus Image Restoration
Uyen Phan, Ozer Can Devecioglu, Serkan Kiranyaz +1
Diabetic retinopathy is a leading cause of vision impairment, making its early diagnosis through fundus imaging critical for effective treatment planning. However, the presence of…
Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration
Ilke Adalioglu, Serkan Kiranyaz, Mete Ahishali +5
Echocardiography is the most widely used imaging to monitor cardiac functions, serving as the first line in early detection of myocardial ischemia and infarction. However, echocard…
Channel-wise Feature Decorrelation for Enhanced Learned Image Compression
Farhad Pakdaman, Moncef Gabbouj
The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance. This app…
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs
Yuxin Xie, Li Yu, Farhad Pakdaman +1
Noisy images are a challenge to image compression algorithms due to the inherent difficulty of compressing noise. As noise cannot easily be discerned from image details, such as hi…
Panoramic Image Inpainting With Gated Convolution And Contextual Reconstruction Loss
Li Yu, Yanjun Gao, Farhad Pakdaman +1
Deep learning-based methods have demonstrated encouraging results in tackling the task of panoramic image inpainting. However, it is challenging for existing methods to distinguish…
Perceptual Learned Image Compression via End-to-End JND-Based Optimization
Farhad Pakdaman, Sanaz Nami, Moncef Gabbouj
Emerging Learned image Compression (LC) achieves significant improvements in coding efficiency by end-to-end training of neural networks for compression. An important benefit of th…
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution
Alexander Ulrichsen, Paul Murray, Stephen Marshall +4
Hyperspectral Imaging is a crucial tool in remote sensing which captures far more spectral information than standard color images. However, the increase in spectral information com…
R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classification
Mete Ahishali, Aysen Degerli, Serkan Kiranyaz +3
Restoration of poor quality images with a blended set of artifacts plays a vital role for a reliable diagnosis. Existing studies have focused on specific restoration problems such…
Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
Aysen Degerli, Fahad Sohrab, Serkan Kiranyaz +1
Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocard…
OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
Aysen Degerli, Serkan Kiranyaz, Muhammad E. H. Chowdhury +1
Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep…
Reliable COVID-19 Detection Using Chest X-ray Images
Aysen Degerli, Mete Ahishali, Serkan Kiranyaz +2
Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning al…
Early Detection of Myocardial Infarction in Low-Quality Echocardiography
Aysen Degerli, Morteza Zabihi, Serkan Kiranyaz +4
Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32.4 million people suffer each year. Early diagnosis and t…
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection
Serkan Kiranyaz, Aysen Degerli, Tahir Hamid +7
Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly kno…
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
Mete Ahishali, Aysen Degerli, Mehmet Yamac +6
Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning…
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Mehmet Yamac, Mete Ahishali, Aysen Degerli +3
Coronavirus disease (Covid-19) has been the main agenda of the whole world since it came in sight in December 2019. It has already caused thousands of causalities and infected seve…