In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality
of image analysis. In general, the accuracy of this process may depend both on the …

In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality
of image analysis. In general, the accuracy of this process may depend both on the …

In recent years, deep convolutional neural networks have shown fascinating performance in
the field of image denoising. However, deeper network architectures are often accompanied …

The accuracy of medical imaging-based diagnostics is directly impacted by the quality of the
collected images. A passive approach to improve image quality is one that lags behind …

Image denoising, particularly Gaussian denoising, has achieved continuous success in the
past decades. Although deep convolutional neural networks (CNNs) are also shown leading …

Deep convolutional neural networks and generative adversarial networks currently attracted
the attention of researchers because it is more effective than conventional representation …

Real-world blind denoising poses a unique image restoration challenge due to the non-
deterministic nature of the underlying noise distribution. Prevalent discriminative networks …

Image denoising has recently witnessed substantial progress. However, many existing
methods remain suboptimal for texture restoration due to treating different image regions …

In the last several years deep learning based approaches have come to dominate many
areas of computer vision, and image denoising is no exception. Neural networks can learn …

Deep convolutional neural networks (CNNs) have achieved considerable success with
image denoising. However, they still lack consistent performance across different noise …