The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …

Camouflaged object segmentation (COS) aims to identify objects that are" perfectly"
assimilate into their surroundings, which has a wide range of valuable applications. The key …

Global context information is particularly important for comprehensive scene understanding.
It helps clarify local confusions and smooth predictions to achieve fine-grained and coherent …

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem.
However, CNNs depend on deeper network architectures to improve performance of image …

Small object detection remains an unsolved challenge because it is hard to extract the
information of small objects with only a few pixels. While scale-level corresponding detection …

Convolutional neural networks (CNNs) have obtained remarkable performance via deep
architectures. However, these CNNs often achieve poor robustness for image super …

Image super-resolution (SR) techniques with deep convolutional network (CNN) have
achieved significant improvements compared to previous shallow-learning-based methods …

Deep convolutional neural networks (CNNs) have been popularly adopted in image super-
resolution (SR). However, deep CNNs for SR often suffer from the instability of training …

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision
over the past five years. According to the nature of different applications, designing …

Stereo image super-resolution exploits additional features from cross view image pairs for
high resolution (HR) image reconstruction. Recently, several new methods have been …