Geometric Moment Invariants to Motion Blur
This work addresses motion blur interference in image analysis for applications such as image retrieval and classification, representing an incremental improvement by extending geometric moment invariants to handle motion blur.
The paper tackled the problem of motion blur interference by deriving motion blur invariants from geometric moments, proving a linear relationship between blurred and original image moments, and found that some invariants are robust to both spatial transforms and motion blur, with results showing they outperform existing blur moment invariants and non-moment features in tasks like image retrieval and classification.
In this paper, we focus on removing interference of motion blur by the derivation of motion blur invariants.Unlike earlier work, we don't restore any blurred image. Based on geometric moment and mathematical model of motion blur, we prove that geometric moments of blurred image and original image are linearly related. Depending on this property, we can analyse whether an existing moment-based feature is invariant to motion blur. Surprisingly, we find some geometric moment invariants are invariants to not only spatial transform but also motion blur. Meanwhile, we test invariance and robustness of these invariants using synthetic and real blur image datasets. And the results show these invariants outperform some widely used blur moment invariants and non-moment image features in image retrieval, classification and template matching.