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Conference Paper (international conference)

Improving Neural Blind Deconvolution

Kotera Jan, Šmídl Václav, Šroubek Filip

: 2021 IEEE International Conference on Image Processing : Proceedings, p. 1954-1958

: IEEE International Conference on Image Processing (ICIP) 2021, (Anchorage, US, 20210919)

: GA20-27939S, GA ČR

: blind deblurring, SelfDeblur, deep image prior

: 10.1109/ICIP42928.2021.9506502

: http://library.utia.cas.cz/separaty/2021/ZOI/kotera-0546240.pdf

(eng): The field of blind image deblurring was for a long time dominated by Maximum-A-Posteriori methods seeking the optimal pair of sharp image--blur of a suitable functional. Recently, learning-based methods, especially those based on deep convolutional neural networks, are proving effective and are receiving increasing attention by the research community. In 2020, Ren~et~al. proposed a deblurring method called SelfDeblur which combines the model-driven approach of traditional MAP methods and the generative power of neural nets. The method is capable of producing very high-quality results, yet it inherits some problems of MAP methods, especially possible convergence to a wrong local optimum. In this paper we propose several easy-to-implement modifications of SelfDeblur, namely suitable initialization, multiscale processing, and regularization, that improve the average performance of the original method and decrease the probability of failure.

: JD

: 20204

2019-01-07 08:39