The past decade has witnessed the great success of deep learning in many disciplines,
especially in computer vision and image processing. However, deep learning-based video …

Recently, deep convolutional neural networks (CNNs) have been successfully applied to the
single-image super-resolution (SISR) task with great improvement in terms of both peak …

Deep neural networks (DNNs) have shown very promising results for various image
restoration (IR) tasks. However, the design of network architectures remains a major …

In recent years, convolutional neural networks have achieved considerable success in
different computer vision tasks, including image denoising. In this work, we present a …

Significant advances in video compression systems have been made in the past several
decades to satisfy the near-exponential growth of Internet-scale video traffic. From the …

Image restoration is a long-standing problem in image processing and low-level computer
vision. Recently, discriminative convolutional neural network (CNN)-based approaches …

Video data has become the largest source of data consumed globally. Due to the rapid
growth of video applications and boosting demands for higher quality video services, video …

To realize the multi-focus image fusion task, an end-to-end deep convolutional neural
network (DCNN) model that produces the final fused image directly from the source images …

In this paper, a vision‐based crash detection framework was proposed to quickly detect
various crash types in mixed traffic flow environment, considering low‐visibility conditions …

As a powerful statistical signal modeling technique, sparse representation has been widely
used in various image restoration (IR) applications. The sparsity-based methods have …