Wavelet-Domain Masked Image Modeling for Color-Consistent HDR Video Reconstruction
Yang Zhang, Zhangkai Ni, Wenhan Yang, Hanli Wang

TL;DR
This paper introduces WMNet, a wavelet-domain masked image modeling approach for HDR video reconstruction that enhances color accuracy and temporal consistency using novel modules and a new benchmark dataset.
Contribution
The paper presents WMNet, a novel HDR video reconstruction network employing wavelet domain masked image modeling and new modules for improved color and temporal fidelity.
Findings
Achieves state-of-the-art performance in HDR video reconstruction
Significantly improves color fidelity and temporal coherence
Introduces HDRTV4K-Scene benchmark dataset
Abstract
High Dynamic Range (HDR) video reconstruction aims to recover fine brightness, color, and details from Low Dynamic Range (LDR) videos. However, existing methods often suffer from color inaccuracies and temporal inconsistencies. To address these challenges, we propose WMNet, a novel HDR video reconstruction network that leverages Wavelet domain Masked Image Modeling (W-MIM). WMNet adopts a two-phase training strategy: In Phase I, W-MIM performs self-reconstruction pre-training by selectively masking color and detail information in the wavelet domain, enabling the network to develop robust color restoration capabilities. A curriculum learning scheme further refines the reconstruction process. Phase II fine-tunes the model using the pre-trained weights to improve the final reconstruction quality. To improve temporal consistency, we introduce the Temporal Mixture of Experts (T-MoE) module…
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Taxonomy
TopicsImage Enhancement Techniques · Generative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques
