The establishment of an accurate and reliable forecasting model is important for water
resource planning and management. In this study, we developed a hybrid model (namely …

In this paper, we propose a novel approach to the rank minimization problem, termed rank
residual constraint (RRC) model. Different from existing low-rank based approaches, such …

A prominent property of natural images is that groups of similar patches within them tend to
lie on low-dimensional subspaces. This property has been previously used for image …

Recently deep learning-based image compression methods have achieved significant
achievements and gradually outperformed traditional approaches including the latest …

In this paper we present a new patch-based empirical Bayesian video denoising algorithm.
The method builds a Bayesian model for each group of similar space-time patches. These …

As a critical factor of earth's ecosystem, vegetation is sensitive to climate change and its
feedback has a pronounced effect on climate, hydrology, and ecology, etc. The quantitative …

Optical coherence tomography (OCT) is widely used for diagnosing and monitoring retinal
disorders. However, despite hardware improvements, its scans are still highly affected by …

In recent years, there has been a growing interest in applying convolutional neural networks
(CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the …

In recent years, the smart grid has been recognized as an important form of the Internet of
Things application. In the smart grid, as an energy-based cyber-physical system, the …

In image processing, it is well known that mean square error criteria is perceptually
inadequate. Consequently, image quality assessment (IQA) has emerged as a new branch …