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Blur Invariants for Image Recognition
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DOI：10.1007/s11263-023-01798-7.png)
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Blur is an image degradation that makes object recognition challenging. Restoration approaches solve this problem via image deblurring, deep learning methods rely on the augmentation of training sets. Invariants with respect to blur offer an alternative way of describing and recognising blurred images without any deblurring and data augmentation. In this paper, we present an original theory of blur invariants. Unlike all previous attempts, the new theory requires no 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. Applying a general substitution rule, combined invariants to blur and spatial transformations are easy to construct and use. Experimental comparison to Convolutional Neural Networks shows the advantages of the proposed theory.
Keyword:
Blurred image
Object recognition
Blur invariants
Projection operators
Moments
期刊
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9.3
论文数:
4.0K
被引数:
2.8W
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分享收藏Combined blur, translation, scale and rotation invariant image recognition by Radon and pseudo-Fourier-Mellin transforms通过Radon和伪fourier-mellin变换组合模糊，平移，缩放和旋转不变图像识别.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
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