Event-Guided Motion Deblurring with Wavelet-Based Cross-Modal Feature Fusion

Recent studies have shown that event data can effectively handle motion blur and preserve fine details in dynamic scenes, thereby significantly enhancing motion deblurring performance. In this paper, we propose event-guided motion deblurring with wavelet-based cross-modal feature fusion. We design cross-modal deblurring UNet to fuse event data and motion blurred image using discrete wavelet transform (DWT) and cross-modal feature fusion, named DUNet. DUNet performs multi-scale feature fusion based on a UNet architecture and dual-branch cross-modal Transformers to address both global and local blurs in a coarse-to-fine manner. Moreover, DUNet decomposes motion blurred image into four subbands based on DWT to align high-frequency subbands (LH, HL, HH) with the edge-focused event data for complementary fusion while leveraging the low-frequency subband (LL) to preserve global structure and semantic details. For effective fusion of event data and motion blurred image, the dual-branch cross-modal Transformers are employed to reduce computational complexity while effectively capturing global context. Various experiments on GoPro and REBlur datasets show that DUNet generates motion deblurring results with sharp edges, enhanced details, and reduced color distortion, outperforming existing state-of-the-art methods in terms of PSNR, SSIM, and computational complexity.