Single image super-resolution (SISR) has played an important role in the field of image
processing. Recent generative adversarial networks (GANs) can achieve excellent results …

Recently, deep learning approaches using CycleGAN have been demonstrated as a
powerful unsupervised learning scheme for low-dose CT denoising. Unfortunately, one of …

Purpose Deep learning‐based image denoising and reconstruction methods demonstrated
promising performance on low‐dose CT imaging in recent years. However, most existing …

Purpose: Convolutional neural network (CNN)‐based denoising is an effective method for
reducing complex computed tomography (CT) noise. However, the image blur induced by …

Sparse-view Computed Tomography (CT) has the ability to reduce radiation dose and
shorten the scan time, while the severe streak artifacts will compromise anatomical …

Deep Learning approaches for solving Inverse Problems in imaging have become very
effective and are demonstrated to be quite competitive in the field. Comparing these …

Sparse-view computed tomography (CT), an imaging technique that reduces the number of
projections, can reduce the total scan duration and radiation dose. However, sparse data …

Although low-dose CT imaging has attracted a great interest due to its reduced radiation risk
to the patients, it suffers from severe and complex noise. Recent fully-supervised methods …

Background There has been growing interest in low‐dose computed tomography (LDCT) for
reducing the X‐ray radiation to patients. However, LDCT always suffers from complex noise …

Deep learning attempts medical image denoising either by directly learning the noise
present or via first learning the image content. We observe that residual learning (RL) often …