Wiener-Deconvolution-Driven Event-Based Deblurring for Low-Light Imaging

We address event-based deblurring for low-light imaging, where conventional frames suffer severe blur, noise and saturation, while events capture sharp high-frequency contrast changes with microsecond latency that can guide the recovery of lost structures. Existing event-based reconstruction methods neither explicitly model low-light noise and saturation nor enforce precise alignment between events and frames, which limits cross-modal fusion and deblurring quality. We propose the Wiener-Deconvolution-Driven Event-Based Deblurring Network (WiED-Net), which embeds the Wiener deconvolution into a deep architecture so that the physical imaging model and noise statistics are encoded in the frequency domain and high-frequency recovery is stabilized on noise dominated night data. WiED-Net adopts a two stage design. The first stage applies Wiener deconvolution in both image and feature spaces to suppress noise, recover saturated regions and reduce ringing, assisted by an eventguided cross-modal feature fusion (ECFF) module for accurate alignment. The second stage uses a multi-scale fusion module to integrate the complementary event and image branches. Training is constrained by a set of losses, including a tailored blur kernel loss that provides closed-loop regularization from physical priors. Together, these designs enable WiED-Net to recover fine details while robustly suppressing artifacts and noise, and to achieve superior quantitative and qualitative performance, achieving superior quantitative and qualitative performance with a notable improvement of 1.97 dB in PSNR and 5% in SSIM over the previous state-of-the-art methods in low-light deblurring. Code will be available at https://github.com/zhuzifeng38/WiED-Net.