Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance Pursuit

1 code implementation • CVPR 2023 • Xiaohang Wang, Xuanhong Chen, Bingbing Ni, Hang Wang, Zhengyan Tong, Yutian Liu

The ability of scale-equivariance processing blocks plays a central role in arbitrary-scale image super-resolution tasks.

Image Super-Resolution Representation Learning

Learning Continuous Depth Representation via Geometric Spatial Aggregator

1 code implementation • 7 Dec 2022 • Xiaohang Wang, Xuanhong Chen, Bingbing Ni, Zhengyan Tong, Hang Wang

Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision.

Depth Map Super-Resolution

Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable Fineness Via Adaptive Sampling

1 code implementation • 27 Sep 2022 • Zhengyan Tong, Xiaohang Wang, Shengchao Yuan, Xuanhong Chen, Junjie Wang, Xiangzhong Fang

Comparison with existing state-of-the-art oil painting techniques shows that our results have higher fidelity and more realistic textures.

RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling

1 code implementation • 17 Dec 2020 • Xuanhong Chen, Kairui Feng, Naiyuan Liu, Bingbing Ni, Yifan Lu, Zhengyan Tong, Ziang Liu

To alleviate these obstacles, we present the first large-scale spatial precipitation downscaling dataset named RainNet, which contains more than $62, 400$ pairs of high-quality low/high-resolution precipitation maps for over $17$ years, ready to help the evolution of deep learning models in precipitation downscaling.

Sketch Generation with Drawing Process Guided by Vector Flow and Grayscale

1 code implementation • 16 Dec 2020 • Zhengyan Tong, Xuanhong Chen, Bingbing Ni, Xiaohang Wang

Existing pencil sketch algorithms are based on texture rendering rather than the direct imitation of strokes, making them unable to show the drawing process but only a final result.

Translation