Global structure and local detailed texture have different effects on image enhancement
tasks. However, most existing works treated these two components in the same way, without …

Improving the resolution of magnetic resonance (MR) image data is critical to computer-
aided diagnosis and brain function analysis. Higher resolution helps to capture more …

Generative adversarial networks (GAN) have shown great potential for image quality
improvement in low-dose CT (LDCT). In general, the shallow features of generator include …

Visual reasoning between visual images and natural language remains a long-standing
challenge in computer vision. Conventional deep supervision methods target at finding …

While texts related to images convey fundamental messages for scene understanding and
reasoning, text-based visual question answering tasks concentrate on visual questions that …

Remote sensing image scene classification (RSISC) has broad application prospects, but
related challenges still exist and urgently need to be addressed. One of the most important …

Synthetic aperture radar (SAR) image is affected by inherent speckle, greatly hindering its
interpretation and subsequent applications. In SAR images despeckling tasks, it is still …

Convolutional Neural Network (CNN) achieves impressive success in image super-
resolution (SR), where global context interaction is critical for reconstructing reliable edge …

Recently, multi-resolution pyramid-based techniques have emerged as the prevailing
research approach for image super-resolution. However, these methods typically rely on a …

Single image super-resolution (SISR) with deep convolutional neural networks has recently
attracted increasing attention due to its potentials to generate rich details. To obtain better …