MPRNet (Multi-Stage Progressive Restoration Network)

MPRNet is a specialized deep learning architecture designed for the synergistic restoration of degraded images. It addresses the fundamental trade-off between spatial details and high-level contextual information by employing a multi-stage approach. The network progressively restores images across three stages, ensuring that earlier stages focus on contextual recovery while later stages refine local textures. Key to its 2026 utility is the Supervised Attention Module (SAM), which provides ground-truth signal supervision at every stage, and the Cross-Stage Feature Fusion (CSFF) mechanism that prevents the loss of fine-grained spatial information. In the modern market, MPRNet is a benchmark for academic research and a robust backbone for commercial applications in autonomous driving (visibility in rain), satellite imaging enhancement, and legacy media restoration. Its modular design allows it to be adapted for various degradation types, making it a versatile tool for developers building high-precision visual pipelines where PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) are critical performance indicators.