A Graduated Non-Convexity Technique for Dealing Large Point Spread Functions.
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| Title: | A Graduated Non-Convexity Technique for Dealing Large Point Spread Functions. |
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| Authors: | Boccuto, Antonio, Gerace, Ivan, Giorgetti, Valentina |
| Source: | Applied Sciences (2076-3417); May2023, Vol. 13 Issue 10, p5861, 28p |
| Subject Terms: | TOEPLITZ matrices, IMAGE reconstruction, SYMMETRIC matrices, ENERGY function, IMAGE denoising, IMAGE reconstruction algorithms |
| Abstract: | This paper focuses on reducing the computational cost of a GNC Algorithm for deblurring images when dealing with full symmetric Toeplitz block matrices composed of Toeplitz blocks. Such a case is widespread in real cases when the PSF has a vast range. The analysis in this paper centers around the class of gamma matrices, which can perform vector multiplications quickly. The paper presents a theoretical and experimental analysis of how γ -matrices can accurately approximate symmetric Toeplitz matrices. The proposed approach involves adding a minimization step for a new approximation of the energy function to the GNC technique. Specifically, we replace the Toeplitz matrices found in the blocks of the blur operator with γ -matrices in this approximation. The experimental results demonstrate that the new GNC algorithm proposed in this paper reduces computation time by over 20 % compared with its previous version. The image reconstruction quality, however, remains unchanged. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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