HDRMamba: High Dynamic Range Video Reconstruction Based on State Space Models

High dynamic range (HDR) video reconstruction remains a challenging task, particularly when dealing with dynamic scenes and moving objects, which can easily cause ghosting artifacts. While recent deep learning approaches have demonstrated possess deghosting capabilities, they often struggle with computational efficiency. In this work, we present HDRMamba, an architecture designed to address these limitations by leveraging State Space Models. Our method adopts an improved Mamba architecture, incorporating both spatial and temporal scanning mechanisms, which enable more efficient modeling of long-range dependencies across multiple frames while maintaining linear computational complexity. Additionally, we propose a prompt-tuning method that allows the model to adjust its features based on the exposure level of the input reference frame. Experimental results demonstrate that HDRMamba offers a solution that balances performance with efficient computation, making it a promising solution for real-world HDR video applications.