Search Results for author: Jan P. Allebach

Found 6 papers, 3 papers with code

Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis

2 code implementations12 Apr 2021 Xiaoyu Xiang, Ding Liu, Xiao Yang, Yiheng Zhu, Xiaohui Shen, Jan P. Allebach

In this paper, we explore the open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of that class are missing in the training data.

Domain Adaptation Image-to-Image Translation +1

Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution

no code implementations18 Oct 2020 Xiaoyu Xiang, Qian Lin, Jan P. Allebach

In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with an arbitrary quality factor by exploring joint compression artifacts reduction (CAR) and super-resolution (SR) tasks.

Face Detection Optical Character Recognition +3

Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

3 code implementations CVPR 2020 Xiaoyu Xiang, Yapeng Tian, Yulun Zhang, Yun Fu, Jan P. Allebach, Chenliang Xu

Rather than synthesizing missing LR video frames as VFI networks do, we firstly temporally interpolate LR frame features in missing LR video frames capturing local temporal contexts by the proposed feature temporal interpolation network.

Space-time Video Super-resolution Video Frame Interpolation +1

Multi-View Matching Network for 6D Pose Estimation

no code implementations27 Nov 2019 Daniel Mas Montserrat, Jianhang Chen, Qian Lin, Jan P. Allebach, Edward J. Delp

Applications that interact with the real world such as augmented reality or robot manipulation require a good understanding of the location and pose of the surrounding objects.

6D Pose Estimation Object Detection

Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning

no code implementations CVPR 2017 Yandong Guo, Cheng Lu, Jan P. Allebach, Charles A. Bouman

Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio.

Dictionary Learning Image Compression

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