Blur Invariants for Image Recognition
This addresses image recognition challenges in computer vision by providing a robust method for handling blur, though it appears incremental as it builds on earlier invariants.
The paper tackled the problem of recognizing blurred images without deblurring by developing a unified theory of blur invariants that does not require prior knowledge of blur type, and experimental results showed advantages over concurrent approaches.
Blur is an image degradation that is difficult to remove. Invariants with respect to blur offer an alternative way of a~description and recognition of blurred images without any deblurring. In this paper, we present an original unified theory of blur invariants. Unlike all previous attempts, the new theory does not require any prior knowledge of the blur type. The invariants are constructed in the Fourier domain by means of orthogonal projection operators and moment expansion is used for efficient and stable computation. It is shown that all blur invariants published earlier are just particular cases of this approach. Experimental comparison to concurrent approaches shows the advantages of the proposed theory.