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Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution by Qiu, Zhongwei; Yang, Huan; Fu, Jianlong; Liu, Daochang; Xu, Chang; Fu, Dongmei is a scholarly article available to read on EtoBox.

What is Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution about?

Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges are remained to effectively extract and transmit high-quality textures from high-degraded low-quality sequences, such as blur, additive noises, and compression artifacts. In this work, a novel Frequency-Transformer (FTVSR) is proposed for handling low-quality videos that carry out self-attention in a combined space-time-frequency domain. First, video frames are split into patches and each patch is transformed into spectral maps in which each channel represents a frequency band. It permits a fine-grained self-attention on each frequency band, so that real visual texture can be distinguished from artifacts. Second, a novel dual frequency attention (DFA) mechanism is proposed to capture the global frequency relations and local frequency relations, which can handle different complicated degradation processes in real-world scenarios. Third, we explore different self-attention schemes for video processin

Author

Qiu, Zhongwei; Yang, Huan; Fu, Jianlong; Liu, Daochang; Xu, Chang; Fu, Dongmei

Published

2022

Language

EN