Classification of degraded signals by the method of invariants
Signal Processing
Invariants to Convolution in Arbitrary Dimensions
Journal of Mathematical Imaging and Vision
Recognition of the blurred image by complex moment invariants
Pattern Recognition Letters
Image reconstruction from a complete set of similarity invariants extracted from complex moments
Pattern Recognition Letters
Image analysis by discrete orthogonal Racah moments
Signal Processing
Image analysis by discrete orthogonal dual Hahn moments
Pattern Recognition Letters
Image Analysis Using Hahn Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A local Tchebichef moments-based robust image watermarking
Signal Processing
Moments and Moment Invariants in Pattern Recognition
Moments and Moment Invariants in Pattern Recognition
Image analysis by Bessel-Fourier moments
Pattern Recognition
Image representation using accurate orthogonal Gegenbauer moments
Pattern Recognition Letters
Image analysis by Gaussian-Hermite moments
Signal Processing
Image Normalization by Complex Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
| Hi-index | 0.08 |
An image can be reconstructed from the finite set of its orthogonal moments. Since geometric and complex moment kernels do not satisfy orthogonality criterion, direct image reconstruction using them is deemed to be difficult. In this paper, we propose a technique to reconstruct an image from either geometric moments (GMs) or complex moments (CMs). We utilize a relationship between GMs and Stirling numbers of the second kind. Then, by using the invertibility property of the Stirling transform, the original image can be reconstructed from its complete set of either geometric or complex moments. Further, based on previous works on blur effects on a moment domain and using the proposed reconstruction methods, a formulation is shown to obtain an estimated original image from the degraded image moments and the blur parameter. The reconstruction performance of the proposed methods on blur images is presented to validate the theoretical framework.