Learning Deep ISP for High-Speed Cameras: Achieving DSLR-Quality Imaging Under High Frame Rates

High-speed imaging, which captures the fleeting dynamics of moving objects at extreme frame rates, has become an indispensable tool across a wide range of scientific disciplines. Yet, the pursuit of high temporal resolution often comes at the cost of significant image degradation, due to the inherent limitations of imaging sensors and the extreme conditions of ultra-short exposure and massive data throughput. As a result, high-speed cameras often produce images marred by strong noise and severe color distortions. In this work, we propose a deep image signal processing (ISP) paradigm that enables high-speed cameras to maintain extremely high frame rates while achieving image quality comparable to that of digital single-lens reflex (DSLR) cameras. To this end, we make two key contributions: 1) constructing RHID, the first large-scale real-world high-speed imaging ISP dataset, comprising 282,912 RAW images captured by high-speed cameras and corresponding sRGB images captured by DSLRs, featuring complex degradations intrinsic to high-speed acquisition; and 2) proposing a misalignment-robust ISP learning framework (MisISP), equipped with a prior mapper-guided image alignment module (PMIA) and a spectrum-guided weakly-aligned image supervisory loss, which effectively addresses inherent pixel misalignments caused by heterogeneous sensor characteristics. Extensive experiments demonstrate that our paradigm substantially advances the performance of existing deep ISP models for high-speed imaging, achieving remarkable improvements in noise suppression, brightness enhancement, and color preservation.