首页|Dual Frequency Transformer for Efficient SDR-to-HDR Translation

Dual Frequency Transformer for Efficient SDR-to-HDR Translation

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
The SDR-to-HDR translation technique can convert the abundant standard-dynamic-range(SDR)media resources to high-dynamic-range(HDR)ones,which can represent high-contrast scenes,providing more realistic visual experiences.While recent vision Transformers have achieved promising performance in many low-level vision tasks,there are few works attempting to leverage Trans-formers for SDR-to-HDR translation.In this paper,we are among the first to investigate the performance of Transformers for SDR-to-HDR translation.We find that directly using the self-attention mechanism may involve artifacts in the results due to the inappropriate way to model long-range dependencies between the low-frequency and high-frequency components.Taking this into account,we ad-vance the self-attention mechanism and present a dual frequency attention(DFA),which leverages the self-attention mechanism to sep-arately encode the low-frequency structural information and high-frequency detail information.Based on the proposed DFA,we further design a multi-scale feature fusion network,named dual frequency Transformer(DFT),for efficient SDR-to-HDR translation.Extens-ive experiments on the HDRTV1K dataset demonstrate that our DFT can achieve better quantitative and qualitative performance than the recent state-of-the-art methods.The code of our DFT is made publicly available at https://github.com/CS-GangXu/DFT.

Standard-dynamic-range to high-dynamic-range(SDR-to-HDR)translationTransformerdual frequency attention(DFA)frequency-aware feature decompositionefficient model

Gang Xu、Qibin Hou、Ming-Ming Cheng

展开 >

Tianjin Media Computing Center,College of Computer Science,Nankai University,Tianjin 300000,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaFundamental Research Funds for Central Universities,China

61922046622761452018AAA010040063223049

2024

10.1007/s11633-023-1418-8

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(3)
  • 53