Abstract
The full-image based kernel estimation strategy is usually susceptible by the smooth and fine-scale background regions impacting and it is time-consuming for large-size image deblurring. Since not all the pixels in the blurred image are informative and it is frequent to restore human-interested objects in the foreground rather than background, we propose a novel concept “SalientPatch” to denote informative regions for better blur kernel estimation without user guidance by computing three cues (objectness probability, structure richness and local contrast). Although these cues are not new, it is innovative to integrate and complement each other in motion blur restoration. Experiments demonstrate that our SalientPatch-based deblurring algorithm can significantly speed up the kernel estimation and guarantee high-quality recovery for large-size blurry images as well.
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Acknowledgements
This work is supported in part by National Natural Science Foundation of China with Nos. 61620106003, 61671451, 61572405, 61502490, 61571439, 61771026, in part by the Open Projects Program of National Laboratory of Pattern Recognition with No.201600038, and in part by the Independent Research Project of National Laboratory of Pattern Recognition with No.Z-2018005 and Project 6140001010207.
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Ma, C., Zhang, J., Xu, S. et al. Accurate blind deblurring using salientpatch-based prior for large-size images. Multimed Tools Appl 77, 28077–28100 (2018). https://doi.org/10.1007/s11042-018-6009-2
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DOI: https://doi.org/10.1007/s11042-018-6009-2