Object detection or localization is an incremental step in progression from coarse to fine
digital image inference. It not only provides the classes of the image objects, but also …

Both convolutional and recurrent operations are building blocks that process one local
neighborhood at a time. In this paper, we present non-local operations as a generic family of …

In this paper, we address a rain removal problem from a single image, even in the presence
of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model …

One of the most challenging problems in reconstructing a high dynamic range (HDR) image
from multiple low dynamic range (LDR) inputs is the ghosting artifacts caused by the object …

Image deconvolution is the ill-posed problem of recovering a sharp image, given a blurry
one generated by a convolution. In this work, we deal with space-invariant nonblind …

Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with
both accelerated scan and reduced projection/back-projection calculation. Despite the rapid …

Transferring image-based object detectors to the domain of videos remains a challenging
problem. Previous efforts mostly exploit optical flow to propagate features across frames …

Low-dose CT is an effective solution to alleviate radiation risk to patients, it also introduces
additional noise and streak artifacts. In order to maintain a high image quality for low-dose …

The ability for an agent to localize itself within an environment is crucial for many real-world
applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) …

Deep neural networks have been applied to image restoration to achieve the top-level
performance. From a neuroscience perspective, the layerwise abstraction of knowledge in a …