Scanning electron microscopy (SEM) is of great significance for analyzing the ultrastructure.
However, due to the requirements of data throughput and electron dose of biological …

Scanning electron microscope (SEM) enables imaging of micro-nano scale objects. It is an
analytical tool widely used in the material, earth and life sciences. However, SEM images …

Recently, discriminative learning methods have been widely upsurge in image denoising.
Many of the current methods rely on simplistic noise assumptions. However, in certain …

With the achievements of deep learning, applications of deep convolutional neural networks
for the image denoising problem have been widely studied. However, these methods are …

In image denoising, deep convolutional neural networks (CNNs) can obtain favorable
performance on removing spatially invariant noise. However, many of these networks cannot …

Images are easily polluted by noise in the process of acquisition and transmission, which
will affect people's understanding and utilization of knowledge and information in images …

Electron microscopy (EM) image denoising is critical for visualization and subsequent
analysis. Despite the remarkable achievements of deep learning-based non-blind denoising …

High-frequency regions like edges compromise the image denoising performance. In
traditional hand-crafted systems, image edges/textures were regularly used to restore the …

Convolutional neural networks (CNNs) have performed well in image denoising. Although
some CNNs enlarge convolutional kernels and increase stacked convolutional layers to …

Abstract Convolutional Neural Network has achieved great success in image denoising. The
conventional methods usually sense those beyond scope contextual info at the expense of …