Model-Based Multiframe Radiometric Spatial Reconstruction for Optical Satellite Video

Satellite video provides repeated observations of the same ground scene within short acquisition intervals, but blur, detector sampling, radiometric differences, noise, and registration errors complicate joint reconstruction. This study presents mixed sparse representation-based collaborative quality improvement (MSR-CQI), a model-based method for joint radiometric and spatial reconstruction of short optical satellite video sequences. The method combines multiframe fidelity, effective PSF modeling, an intensity prior, overlapping group sparsity, high-order nonconvex regularization, intensity bounds, and optional static observation weighting. In controlled ×2 experiments with known HR references, MSR-CQI achieved 42.8714 dB PSNR and 0.9756 SSIM. With the same seven input frames, it achieved 43.1049 dB/0.9714, compared with 42.1369 dB/0.9698 for PnP-NLM and 38.1675 dB/0.9404 for DUF-16L. Retaining measured sampling shifts in the observation operators yielded 45.7461 dB/0.98046, versus 45.0380 dB/0.97904 after LR registration and resampling. The proxy derived from the reserved real frames instead favored the common-grid reconstruction, showing that agreement with this proxy does not establish recovery beyond the native sensor resolution. Static observation weighting was also scene-dependent. These results support retaining sampling phases explicitly in controlled spatial SR, while the real-data results after common-grid resampling are interpreted as multiframe restoration and proxy agreement.