This article reviews the deep learning methods for computed tomography image denoising
and deblurring separately and simultaneously. Then, we discuss promising directions in this …

Medical image denoising is considered among the most challenging vision tasks. Despite
the real-world implications, existing denoising methods have notable drawbacks as they …

Abstract Machine learning (ML) has been extensively involved in assistant disease
diagnosis and prediction systems to emancipate the serious dependence on medical …

When deblurring an image, ensuring that the restored intensities are strictly non-negative is
crucial. However, current numerical techniques often fail to consistently produce favorable …

Scanning electron microscopy (SEM) is a crucial tool in the field of materials science,
providing valuable insights into the microstructural characteristics of materials …

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung
scans, our study introduces the Triple Equivariant Motion Transformer (TEMT), an …

Aiming at the non-uniform blurring of image caused by optical system defects or external
interference factors, such as camera shake, out-of-focus, and fast movement of object, a …

Introduction: Pneumocephalus and pneumorrhachis are rare postoperative complications,
commonly occurring within a few days to months after spinal surgery. They are very rarely …

In the process of modern medical diagnosis, medical image‐assisted diagnosis plays a very
important role. However, the process of medical image acquisition, will be affected by …

The goal of blind image restoration is to recover the latent clean image from the observed
blur version without complete prior information of the degraded factor. Commonly, blind …