DocumentCode :
3748498
Title :
Class-Specific Image Deblurring
Author :
Saeed Anwar;Cong Phuoc Huynh;Fatih Porikli
Author_Institution :
Australian Nat. Univ., Canberra, ACT, Australia
fYear :
2015
Firstpage :
495
Lastpage :
503
Abstract :
In image deblurring, a fundamental problem is that the blur kernel suppresses a number of spatial frequencies that are difficult to recover reliably. In this paper, we explore the potential of a class-specific image prior for recovering spatial frequencies attenuated by the blurring process. Specifically, we devise a prior based on the class-specific subspace of image intensity responses to band-pass filters. We learn that the aggregation of these subspaces across all frequency bands serves as a good class-specific prior for the restoration of frequencies that cannot be recovered with generic image priors. In an extensive validation, our method, equipped with the above prior, yields greater image quality than many state-of-the-art methods by up to 5 dB in terms of image PSNR, across various image categories including portraits, cars, cats, pedestrians and household objects.
Keywords :
"Kernel","Image restoration","Training","Image edge detection","Band-pass filters","Frequency-domain analysis","Minimization"
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2015 IEEE International Conference on
Electronic_ISBN :
2380-7504
Type :
conf
DOI :
10.1109/ICCV.2015.64
Filename :
7410421
Link To Document :
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