MIRNet

MIRNet is a revolutionary neural network architecture introduced for image restoration tasks, specifically designed to address the challenge of maintaining high-resolution representations while processing at multiple scales. In the 2026 market, it stands as a cornerstone for mobile ISP pipelines and professional post-production software. The architecture utilizes a Multi-Scale Residual Block (MRB) that allows for recursive information exchange across parallel multi-resolution streams. Unlike traditional U-Net structures that lose spatial precision through aggressive downsampling, MIRNet maintains high-resolution features throughout the network, selectively fusing them with low-resolution context using Selective Kernel Feature Fusion (SKFF). This makes it exceptionally potent for tasks like low-light enhancement, where capturing both global illumination and fine local texture is critical. As an open-source model available primarily through PyTorch and TensorFlow implementations, it has been widely adopted by developers for edge-device deployment, enabling real-time image cleanup in smartphones and surveillance systems. Its technical positioning in 2026 focuses on high-fidelity reconstruction where 'hallucination' (typical of GANs) must be minimized in favor of mathematical accuracy, making it a preferred choice for medical and forensic imaging.