A Unified Framework for Deblurring and HDR Imaging in Dynamic Scenes
Xiaowen Ma, Kangbiao Shi, Daijin Chen, Yu Cao, Qingsen Yan, Yanning Zhang
Abstract
High Dynamic Range (HDR) imaging endeavors to enhance the visual appeal of an image by merging multi-exposure images. However, images captured with hand-held cameras often suffer from severe motion blur and ghosting artifacts in dynamic scenes. To address these challenges, we propose Image Deblurring and HDR Imaging (ID-HDRI), an end-to-end joint optimization framework that directly recovers sharp details and reconstructs HDR images from blurred, multi-exposure inputs. The proposed framework employs a dual-branch architecture that divides the encoder into two individual branches, each dedicated to a specific task. This innovative design facilitates the separate learning of degradation features. Furthermore, to fully utilize the potential of these features, we introduce multi-scale fusion and gate fusion modules, which play key roles in the entire network. Additionally, we build a new dataset comprising images with varying exposure levels and blurriness for HDR imaging in dynamic settings. Finally, ablation analyses demonstrate the efficacy of these fusion modules in enhancing HDR image reconstruction performance. Compared to traditional pipelines that sequentially perform CNN-based deblurring and HDR imaging, our method exhibits superior performance in both objective metrics and subjective visual quality