Blur Invariants for Image Recognition
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 103 references that are in the map. First posted on arXiv 18 Jan 2023.
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4 of 103 references.
References · 4
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016Understanding How Image Quality Affects Deep Neural Networkscited by 119
- 2016Examining the Impact of Blur on Recognition by Convolutional Networkscited by 58
- 2017On Classification of Distorted Images with Deep Convolutional Neural Networkscited by 26
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