# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 1 Introduction  
The proliferation of mobile smart devices has made video recording an essential part of daily life. However, it is recognized that captured videos can experience blur as a result of camera shake and object motion. Therefore, the development and application of effective video deblurring algorithms are highly beneficial for improving video quality and enhancing the overall viewing experience. Moreover, video deblurring serves as a crucial preprocessing step for high-level vision tasks, such as action recognition [1], object detection [2], and visual tracking [3], where the quality of input videos significantly impacts the downstream performance. Video deblurring remains a challenging restoration task, with its core difficulty lying in effectively utilizing temporal information from video sequences. Research [4] has shown that appropriate feature alignment strategies are vital for improving deblurring performance. Early methods primarily employed optical flow [5, 6, 7] for explicit alignment, but blur-induced inaccuracies in flow estimation propagated errors that compromised reconstruction quality. To address this limitation, researchers developed adaptive feature alignment schemes based on deformable convolution [8, 9, 10]. Recently, transformers have emerged as a promising solution [11, 12, 13] for video deblurring, demonstrating superior performance in complex scenarios through their powerful self-attention mechanisms and long-range dependency modeling capabilities. However, video deblurring faces unique challenges compared to other video restoration tasks, like super-resolution. Blur not only results in the loss of high-frequency details but also significantly distorts the fundamental image structure. While existing methods have achieved remarkable progress in detail recovery, they often overlook the equally important aspect of structural information reconstruction. Therefore, effectively addressing the simultaneous management of high-frequency details and low-frequency structural information in video deblurring continues to pose a significant challenge within the field of research. To address these issues, this paper proposes a wavelet-based, blur-aware decoupled network (WBDNet) that decouples structure recovery from detail enhancement. Unlike previous approaches [14; 15; 16] that merely used wavelet transforms for multi-scale feature extraction, we design specialized recovery strategies for different frequency bands. Specifically, we introduce a multi-scale progressive fusion (MSPF) module and a blur-aware detail enhancement (BADE) module. In the low-frequency domain, the MSPF module operates in several steps. It first employs optical flow for coarse alignment. Then, it constructs multi-scale feature pyramids and uses bottom-up progressive feature fusion to reconstruct the main image structure. In the high-frequency domain, the BADE module takes a different approach. It integrates blur-aware attention mechanisms with deformable convolution. This integration enhances features in sharp regions, thereby enabling more refined alignment. As a result, this approach enables the effective extraction and integration of valuable detailed information from multiple frames. Our main contributions can be summarized as follows:  
* We propose a novel multi-scale progressive fusion (MSPF) module that effectively reconstructs structural information through multi-scale feature fusion, significantly improving the restoration of low-frequency components. * We design an innovative blur-aware detail enhancement module (BADE) that adaptively perceives and extracts sharp features across multiple frames for precise detail recovery, enabling more effective high-frequency information restoration. * Experimental results on benchmark datasets demonstrate that our proposed method achieves significant performance improvements over state-of-the-art approaches, particularly in preserving both structural integrity and detail fidelity.