This paper introduces a new framework for image registration, focusing on a multilevel inexact Gauss-Newton method combined with a multigrid solver. The authors address the challenge of finding a transformation that aligns a reference image \( R \) with a template image \( T \) by minimizing a joint energy function that balances similarity and regularity. The optimization problem is discretized using staggered grids, ensuring h-ellipticity independent of parameter choices, which facilitates efficient multigrid implementation. To handle large nonlinearities and speed up computation, a multilevel continuation technique is employed. The method is demonstrated on a highly nonlinear registration problem involving 3D MRI scans of a human knee, showing significant reduction in the image distance and efficient performance on modest computational hardware. The paper also discusses the theoretical properties of the discretization and the effectiveness of the multigrid solver, providing numerical examples to support the theoretical findings.This paper introduces a new framework for image registration, focusing on a multilevel inexact Gauss-Newton method combined with a multigrid solver. The authors address the challenge of finding a transformation that aligns a reference image \( R \) with a template image \( T \) by minimizing a joint energy function that balances similarity and regularity. The optimization problem is discretized using staggered grids, ensuring h-ellipticity independent of parameter choices, which facilitates efficient multigrid implementation. To handle large nonlinearities and speed up computation, a multilevel continuation technique is employed. The method is demonstrated on a highly nonlinear registration problem involving 3D MRI scans of a human knee, showing significant reduction in the image distance and efficient performance on modest computational hardware. The paper also discusses the theoretical properties of the discretization and the effectiveness of the multigrid solver, providing numerical examples to support the theoretical findings.