Search Results for author:
Found 10 papers, 7 papers with code
Cross-Camera Deep Colorization
Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system.
EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation
In this paper, we consider the problem of reference-based video super-resolution(RefVSR), i. e., how to utilize a high-resolution (HR) reference frame to super-resolve a low-resolution (LR) video sequence.
Reference-based Video Super-Resolution
Video Super-Resolution
Zoom in to the details of human-centric videos
Presenting high-resolution (HR) human appearance is always critical for the human-centric videos.
SurfaceNet+: An End-to-end 3D Neural Network for Very Sparse Multi-view Stereopsis
Furthermore, the latter problem is handled via a multi-scale strategy that consequently refines the recovered geometry around the region with the repeating pattern.
SPI-Optimizer: an integral-Separated PI Controller for Stochastic Optimization
Inspired by the conditional integration idea in classical control society, we propose SPI-Optimizer, an integral-Separated PI controller based optimizer WITHOUT introducing extra hyperparameter.
RegNet: Learning the Optimization of Direct Image-to-Image Pose Registration
Direct image-to-image alignment that relies on the optimization of photometric error metrics suffers from limited convergence range and sensitivity to lighting conditions.
CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping
The Reference-based Super-resolution (RefSR) super-resolves a low-resolution (LR) image given an external high-resolution (HR) reference image, where the reference image and LR image share similar viewpoint but with significant resolution gap x8.
SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model.
Deep Learning for Surface Material Classification Using Haptic And Visual Information
When a user scratches a hand-held rigid tool across an object surface, an acceleration signal can be captured, which carries relevant information about the surface.
Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation
Finally, the optimization problem enables us to take advantage of state-of-the-art fully convolutional network structure for the implementation of the above encoders and decoder.
