Get started
Work

Blind Image Deconvolution: When Patch-wise Minimal Pixels Prior Meets Fractional-Order Method

Year: 2024
Type: article
Abstract: Blind image deblurring is a challenging issue in image processing. In blind image deblurring, the typical approach involves iteratively estimating both the blur kernel and latent image until convergence to the blur kernel of the observed image is achieved. Recently, several approaches have been attempted to develop a sophisticated regularization to obtain the clean image. However, existing methods often struggle to effectively handle ringing artifacts and local blur. To overcome these limitations, we introduce a fractional-order variational model. This model alleviates the ringing artifacts through the selection of an optimal derivative. Subsequently, to refine the latent image further, we leverage the local prior, namely Patch-wise Minimal Pixels (PMP) prior. Since the PMP prior of clean image blocks is much sparser than that of blurred ones, it is capable of discriminating between clean and blurred image blocks. We illustrate the effective integration of the fractional-order operations and the PMP prior within our proposed approach. Moreover, the convergence of our algorithm has been proven as the values of the objective function monotonically decrease. Extensive experiments on different datasets demonstrate the superiority of the proposed method compared with other methods in terms of reconstruction quality for blind deblurring.
DOI: 10.1007/s10851-024-01221-x
Authors: Tingting Wu, Shaojie Wan, Chenchen Feng, Hao Zhang, Tieyong Zeng
Language: English
Open Access status: hybrid
Estimated APC paid: $2,990
OpenAlex ID: W4405882314
FWCI: 0.422
Cites: 50
Cited by: 3
Related to: 10