MIRNet (Multi-stage Image Restoration Network) learns enriched feature representations through multi-resolution streams and attention mechanisms to recover high-quality image content from degraded inputs. The model processes images through parallel feature extraction branches at different scales, combining them via selective fusion to preserve spatial details while enhancing contextual information. It achieves state-of-the-art results on benchmark datasets for image denoising, super-resolution, and general image enhancement tasks.