Good Regions to Deblur

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Authors Zhe Hu, Ming-Hsuan Yang
Journal/Conference Name ECCV
Paper Category
Paper Abstract The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image. Recent works focus on exploiting natural image priors or additional image observations for deblurring, but pay less attention to the influence of image structures on estimating blur kernels. What is the useful image structure and how can one select good regions for deblurring? We formulate the problem of learning good regions for deblurring within the Conditional Random Field framework. To better compare blur kernels, we develop an effective similarity metric for labeling training samples. The learned model is able to predict good regions from an input blurred image for deblurring without user guidance. Qualitative and quantitative evaluations demonstrate that good regions can be selected by the proposed algorithms for effective image deblurring.
Date of publication 2012
Code Programming Language MATLAB
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