The growing demand for applications based on Generative Adversarial Networks (GANs)
has prompted substantial study and analysis in a variety of fields. GAN models have …

Liver disease is a potentially asymptomatic clinical entity that may progress to patient death.
This study proposes a multi-modal deep neural network for multi-class malignant liver …

Liver cancer diagnosis requires preprocessing of images with preserved structural details. In
this study, a multi-level generative adversarial network (GAN) is proposed to enhance …

Given the escalating potential risk associated with X-ray radiation exposure to patients,
scholars have been dedicated to investigating advanced algorithms for low-dose CT (LDCT) …

Existing learning-based image deblurring algorithms tend to focus on single source of image
information, and the network structure and dynamic scene blur characteristics make it …

We propose a method to suppress the speckle noise and blur effects of the light field
extracted from a hologram using a deep‐learning technique. The light field can be extracted …

In colleges and universities, resources for ideological and political education are available
in various forms, increasing the complexity and difficulty of resource classification. To …

To produce super-resolution images, it is essential to eliminate the noise elements and give
a clear noisefree output. To achieve this purpose multiscale image representation is found to …