Blind image deconvolution is an ill-posed problem that attempts to restore an acquired image degraded by unknown PSF. A variational BID implementation, called NAS-RIF, is known for being robust but prone to poor convergence under low SNR and unrealistic support. Motivated by simple yet effi cient fi delity metric, this paper presents an improved NAS-RIF by reducing adverse effect of inverse high-pass fi lter and computationally intensive pre-deterministic noise removal, by adaptively incorporating anisotropic structural property within local neighborhood seamlessly in NAS-RIF cost function. With an automatic support region estimation, the entire deconvolution process was fully automatic. The experimental results reported herein indicated that the enhanced structural adaptive anisotropic NAS-RIF had better convergence condition,while maintaining the underlying image fi delity.

Performance of radiographic diagnosis and therapeutic intervention heavily depends on the quality of acquired images. Over decades, a range of preprocessing for image enhancement has been explored. Among the most recent proposals is iterative blinded image deconvolution, which aims to identify the inheritant point spread function, degrading images during acquisition. Thus far, the technique has been known for its poor convergence and stability and was recently superseded by non-negativity and support constraints recursive image filtering. However, the latter requires a priori on intrinsic properties of imaging sensor, e.g., distribution, noise floor and field of view. Most importantly, since homogeneity assumption was implied by deconvolution, recovered degrading function was global, disregarding fidelity of underlying objects. This paper proposes a modified recursive filtering with similar nonnegativity constraints, but also taking into account local anisotropic structure of content. The experiment reported herein demonstrates its superior convergence property, while also preserving crucial image feature.

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