Name
Abstract | ||
|---|---|---|
Non-blind deconvolution has been a long-standing challenge of both image structures preservation and blur and noise removal. However, most existing methods conduct the direct deconvolution on the degraded image, and overlook the difference between low-frequency and high-frequency of the image. Based on the observation that high-frequency (e.g., edges and structures) is more important than low-frequency in image deblurring, we present a novel method for non-blind deconvolution by incorporating the l1 -norm fidelity of image high-frequency. Firstly, the l1 -norm fidelity of image high-frequency is proposed in the overall objective function for image structures preservation and noise suppression, and then alternating minimization iterative method is employed to estimate high-frequency components of the image. Secondly, high-frequency estimations are taken as constraint terms, and least square integration and fast fourier transform are efficiently exploited to recover the ideal image. Finally, experimental simulations demonstrate that the proposed algorithm outperforms other state-of-the-art methods in both subjective and objective assessments. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11042-016-4083-x | Multimedia Tools Appl. |
Keywords | Field | DocType |
Non-blind deconvolution, Image high-frequency, ℓ1 -norm fidelity, Least square integration | Computer vision,Fidelity,Blind deconvolution,Pattern recognition,Deblurring,Computer science,Iterative method,Deconvolution,Wiener deconvolution,Fast Fourier transform,Artificial intelligence,Image restoration | Journal |
Volume | Issue | ISSN |
76 | 22 | 1573-7721 |
Citations | PageRank | References |
0 | 0.34 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peixian Zhuang | 1 | 14 | 7.39 |
| Yue Huang | 2 | 317 | 29.82 |
| Delu Zeng | 3 | 164 | 11.46 |
| Xinghao Ding | 4 | 591 | 52.95 |