.png){kind=icon}
Return
Partial Deconvolution With Inaccurate Blur Kernel
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2017.2764261.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: 1) a partial map in the Fourier domain for modeling kernel estimation error, and 2) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.
Keywords:
Image deblurring
blind deconvolution
blur kernel estimation
E-M algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
Cited Papers
.png){kind=icon}
A Generalized Accelerated Proximal Gradient Approach for Total-Variation-Based Image Restoration
PREAI
ShareSaveShareSaveShareSaveShareSaveDeconvolving Images With Unknown Boundaries Using the Alternating Direction Method of Multipliers
OAAI
OAAIShareSaveShareSave
View more