Semi-Blindly Enhancing Extremely Noisy Videos With Recurrent Spatio-Temporal Large-Span Network
IEEE Transactions on Pattern Analysis and Machine Intelligence(2023)
摘要
Capturing videos under the extremely dark environment is quite challenging for the extremely large and complex noise. To accurately represent the complex noise distribution, the physics-based noise modeling and learning-based blind noise modeling methods are proposed. However, these methods suffer from either the requirement of complex calibration procedure or performance degradation in practice. In this paper, we propose a semi-blind noise modeling and enhancing method, which incorporates the physics-based noise model with a learning-based Noise Analysis Module (NAM). With NAM, self-calibration of model parameters can be realized, which enables the denoising process to be adaptive to various noise distributions of either different cameras or camera settings. Besides, we develop a recurrent Spatio-Temporal Large-span Network (STLNet), constructed with a Slow-Fast Dual-branch (SFDB) architecture and an Interframe Non-local Correlation Guidance (INCG) mechanism, to fully investigate the spatio-temporal correlation in a large span. The effectiveness and superiority of the proposed method are demonstrated with extensive experiments, both qualitatively and quantitatively.
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关键词
Enhancing low-light videos, semi-blind noise modeling, spatio-temporal large-span
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