Background Recent advances in deep learning techniques have led to improved diagnostic
abilities in ophthalmology. A generative adversarial network (GAN), which consists of two …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

In this paper, we present an extensive review on deep learning methods for image
restoration tasks. Deep learning techniques, led by convolutional neural networks, have …

Image enhancement methods are applied to medical images to reduce the noise that they
contain. There are many academic studies in the literature using classical image …

The non-invasive fetal electrocardiogram (fECG) enables easy detection of develo** heart
abnormalities, leading to a significant reduction in infant mortality rate and post-natal …

Advancements in infrastructure management have significantly benefited from automatic
pavement crack detection systems, relying on image processing enhanced by high …

Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often
suffer from different kinds of noise which degrade the performance of downstream tasks …

Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical
diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we …

One of the most prevalent diseases affecting women in recent years is breast cancer. Early
breast cancer detection can help in the treatment, lower the infection risk, and worsen the …

A dual autoencoder employing separable convolutional layers for image denoising and
deblurring is represented. Combining two autoencoders is presented to gain higher …