Image-Internal Operator Calibration for All-in-One Image Restoration

All-in-one image restoration requires adapting shared restoration knowledge to diverse degradations and image content. Representative prompt- and routing-based methods achieve this through feed-forward conditioning, without explicitly optimizing restoration operators on the current observation. We propose CalibIR, an image-internal operator calibration framework that uses each image to learn corrections to the operators used for its restoration. Starting from learned base operators, CalibIR performs a lightweight optimization step on an input-specific feature-prediction objective within the forward pass. To exploit image-wide information while accommodating regional differences, we develop a structured calibration rule that couples channel-specific shared updates with channel-shared local corrections. The shared component aggregates information across the image, whereas the spatially centered local component captures departures from the image-wide adjustment. Both arise from the same optimization objective, enabling coordinated adaptation without maintaining an independent kernel for every channel–location pair. Trained end-to-end under restoration supervision, CalibIR combines knowledge learned across images with operator adaptation learned within each image. Experiments on standard all-in-one image restoration benchmarks show that CalibIR achieves leading average performance.