A none-blind deblurring algorithm  for noisy images via distributed gradient prior

Jiazi Huang; Qi Li; Huajun Feng; Zhihai Xu; Yueting Chen

doi:10.1117/12.2507231

7 November 2018 A none-blind deblurring algorithm for noisy images via distributed gradient prior

Jiazi Huang, Qi Li, Huajun Feng, Zhihai Xu, Yueting Chen

Proceedings Volume 10832, Fifth Conference on Frontiers in Optical Imaging Technology and Applications; 108320S (2018) https://doi.org/10.1117/12.2507231
Event: Fifth Conference on Frontiers in Optical Imaging Technology and Applications, 2018, Changchun, China

Abstract

This paper proposes a none-blind deblurring algorithm for noisy images via distributed gradient prior. The proposed image prior is motivated by observing the gradient properties of noisy images. Based on the prior of image noise's low gradient distribution, we propose an effective optimization method to deal with noisy and blurry images. In this paper, an image-gradient-related distributed factor is introduced to balance image deblurring and denoising. The distributed factor is related to image noise and works adaptively according to different noise levels of blurry images. Richardson-Lucy method is also adopted to achieve a better deconvolution result. Experiments show that our proposed method outperforms other deblurring algorithms in both preserving details and removing noise.

Citation Download Citation

Jiazi Huang, Qi Li, Huajun Feng, Zhihai Xu, and Yueting Chen "A none-blind deblurring algorithm for noisy images via distributed gradient prior", Proc. SPIE 10832, Fifth Conference on Frontiers in Optical Imaging Technology and Applications, 108320S (7 November 2018); https://doi.org/10.1117/12.2507231

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