"Star Sensor Image"

"Data quality constitutes the upper bound of model performance in scientific vision tasks. Prevailing datasets frequently resort to simplified additive Gaussian noise models, which fail to encapsulate the non-stationary and spatially correlated noise characteristics intrinsic to on-orbit CMOS sensors (e.g., stray light noise, random telegraph noise, row\/column fixed pattern noise). To bridge this domain gap, we introduce HiFAHS, a dual-mode simulation framework designed to synthesize training data with extreme physical realism. The pipeline operates through two complementary streams: a physics-based optoelectronic modeling engine (Type-A) and a hybrid augmentation system utilizing authentic sensor substrates (Type-B). Unlike traditional denoising benchmarks that treat noise as a superficial additive layer, the Type-A pipeline simulates the complete photon-to-electron transfer chain. This process is rigorously structured into three sequential phases: Scene Initialization, Optical Rendering, and Optoelectronic Degradation."