Publication:
Multi-field de-interlacing using deformable convolution residual blocks and self-attention

Placeholder

Program

KU Authors

Co-Authors

Advisor

Publication Date

2022

Language

English

Type

Conference proceeding

Journal Title

Journal ISSN

Volume Title

Abstract

Although deep learning has made significant impact on image/video restoration and super-resolution, learned deinterlacing has so far received less attention in academia or industry. This is despite deinterlacing is well-suited for supervised learning from synthetic data since the degradation model is known and fixed. In this paper, we propose a novel multi-field full frame-rate deinterlacing network, which adapts the state-of-the-art superresolution approaches to the deinterlacing task. Our model aligns features from adjacent fields to a reference field (to be deinterlaced) using both deformable convolution residual blocks and self attention. Our extensive experimental results demonstrate that the proposed method provides state-of-the-art deinterlacing results in terms of both numerical and perceptual performance. At the time of writing, our model ranks first in the Full FrameRate LeaderBoard at https://videoprocessing.ai/benchmarks/deinterlacer.html.

Description

Source:

Proceedings - International Conference on Image Processing, ICIP

Publisher:

The Institute of Electrical and Electronics Engineers Signal Processing Society

Keywords:

Subject

Computer Science, Artificial intelligence, Electrical electronics engineering

Citation

Endorsement

Review

Supplemented By

Referenced By

Copy Rights Note

0

Views

0

Downloads

View PlumX Details