Deep learning networks have recently demonstrated yielded impressive progress for multi-
exposure image fusion. However, how to restore realistic texture details while correcting …

Deep learning (DL) has aroused wide attention in hyperspectral unmixing (HU) owing to its
powerful feature representation ability. As a representative of unsupervised DL approaches …

In this paper, we identify and address a serious design bias of existing salient object
detection (SOD) datasets, which unrealistically assume that each image should contain at …

Automatic machine classification of concrete structural defects in images poses significant
challenges because of multitude of problems arising from the surface texture, such as …

Salient object detection has undergone a very rapid development with the blooming of Deep
Neural Network (DNN), which is usually taken as an important preprocessing procedure in …

RGB-D salient object detection aims to perform the pixel-wise localization of salient objects
from both RGB and depth images, whose challenge mainly comes from how to learn …

Existing works mainly focus on how to aggregate multi-level features for salient object
detection, which may generate sub-optimal results due to interference with redundant …

Salient Object Detection (SOD) plays an important role in many image-related multimedia
applications. Although there are many existing research works about the salient object …

U-Net, known for its simple yet efficient architecture, is widely utilized for image processing
tasks and is particularly suitable for deployment on neuromorphic chips. This paper …

Deep-learning based salient object detection methods achieve great improvements.
However, there are still problems existing in the predictions, such as blurry boundary and …