Abstract
Video snapshot compressive imaging (SCI) captures dynamic scene sequences through a two-dimensional (2D) snapshot, fundamentally relying on optical modulation for hardware compression and the corresponding software reconstruction. While mainstream video SCI using random binary modulation has demonstrated success, it inevitably results in temporal aliasing during compression. One-hot modulation, activating only one sub-frame per pixel, provides a promising solution for achieving perfect temporal decoupling, thereby alleviating issues associated with aliasing. However, no algorithms currently exist to fully exploit this potential. To bridge this gap, we propose an algorithm specifically designed for one-hot masks. First, leveraging the decoupling properties of one-hot modulation, we transform the reconstruction task into a generative video inpainting problem and introduce a stochastic differential equation (SDE) of the forward process that aligns with the hardware compression process. Next, we identify limitations of the pure diffusion method for video SCI and propose a novel framework that combines one-step regression initialization with one-step diffusion refinement. Furthermore, to mitigate the spatial degradation caused by one-hot modulation, we implement a dual optical path at the hardware level, utilizing complementary information from another path to enhance the inpainted video. To our knowledge, this is the first work integrating diffusion into video SCI reconstruction. Experiments conducted on synthetic datasets and real scenes demonstrate the effectiveness of our method.
Abstract (translated)
视频快照压缩成像（SCI）通过二维快照捕获动态场景序列，其基础是依赖光学调制进行硬件压缩，并配合相应的软件重建。虽然主流的使用随机二进制调制的视频SCI已经取得了成功，但在压缩过程中不可避免地会产生时域混叠问题。一种称为“一对一”调制的方法，即每次仅激活每个像素的一个子帧，为实现完美的时间解耦提供了一个有前景的解决方案，从而缓解了与混叠相关的问题。然而，目前还没有算法能够充分利用这一潜力。 为了弥补这一空白，我们提出了一种专门针对一对一掩码设计的新算法。首先，利用一对一调制的时间解耦特性，我们将重建任务转化为生成视频插值问题，并引入了一个与硬件压缩过程相匹配的前向随机微分方程（SDE）。接下来，我们识别了纯扩散方法在视频SCI中的局限性，并提出了一种新的框架，该框架结合了一步回归初始化和一步扩散细化。此外，为了缓解一对一调制造成的空间退化问题，我们在硬件层面实施了一个双光路方案，利用另一条路径的互补信息来增强插值后的视频。 据我们所知，这是首次将扩散方法整合到视频SCI重建中的一项工作。在合成数据集和真实场景上的实验展示了我们的方法的有效性。
URL
https://arxiv.org/abs/2512.17578
