Sorry, you need to enable JavaScript to visit this website.

Warp-based methods for image animation estimate a warp
field what do a rearrangement on the pixels of the input image to roughly align with the target image. Current methods
predict accurate warp field by using manually annotated data.
In this paper, we propose a simple method (MAT-net) to predict more precise warp field in self-supervised way. MAT-net
decomposes complex spatial object movement between two

We present a computational accommodation-invariant near-eye display, which relies on imaging with coherent light and utilizes static optics together with convolutional neural network-based preprocessing. The network and the display optics are co-optimized to obtain a depth-invariant display point spread function, and thus relieve the conflict between accommodation and ocular vergence cues that typically exists in conventional near-eye displays.

With the emergence of social media, voluminous video clips are uploaded every day, and retrieving the most relevant visual content with a language query becomes critical. Most approaches aim to learn a joint embedding space for plain textual and visual contents without adequately exploiting their intra-modality structures and inter-modality correlations.

Accurately tracking large tissue motion over a sequence of ultrasound images is critically important to several clinical applications including, but not limited to, elastography, flow imaging, and ultrasound-guided motion compensation. However, tracking in vivo large tissue deformation in 3D is a challenging problem and requires further developments. In this study, we explore a novel tracking strategy that combines Bayesian inference with local polynomial fitting.

We develop a variational retinex algorithm for enhancing single underwater image with multiorder gradient priors of reflectance and illumination.

Benefiting from learning the residual between low resolution (LR) image and high resolution (HR) image, image super-resolution (SR) networks demonstrate superior reconstruction performance in recent studies. However, for the images with rich texture information, the residuals are complex and difficult for networks to learn. To address this problem, we propose a recurrent residual refinement network (RRRN) to gradually refine the residual with a recurrent structure.