InverseNet: A CASP-Inspired Benchmark for Operator Mismatch in Compressive Imaging

Compressive imaging faces a critical sim-to-real crisis: models trained on idealized forward operators fail catastrophically when deployed on real hardware. Operator mismatch -- the gap between assumed and true forward operators -- degrades deep learning reconstruction by 10-21 dB, yet no existing benchmark measures this effect. We introduce InverseNet, the first cross-modality benchmark for operator mismatch in compressive imaging, spanning coded aperture snapshot spectral imaging (CASSI), coded aperture compressive temporal imaging (CACTI), and single-pixel camera (SPC). InverseNet evaluates 11 reconstruction methods under a standardized three-scenario protocol -- ideal (I), mismatched (II), and oracle-corrected (III) -- across 27 test scenes and over 240 experiments. We discover an inverse performance-robustness relationship: methods achieving the highest ideal PSNR suffer the largest mismatch degradation -- confirming that a mediocre algorithm with a correct forward model outperforms a state-of-the-art network with a wrong one. On CACTI, state-of-the-art EfficientSCI loses 20.58 dB under mismatch, while classical GAP-TV recovers 93% of its own mismatch loss through oracle calibration. We further establish a mask-awareness taxonomy -- mask-oblivious architectures show zero calibration benefit, while mask-conditioned methods recover 41-90% of mismatch losses depending on mismatch type. By providing a standardized, reproducible evaluation of operator mismatch across modalities, InverseNet aims to catalyze an "AlphaFold moment" for computational imaging.