An Adaptive Iterative Algorithm for Motion Deblurring Based on Salient Intensity Prior

• Hancheng Yu,
• Wenkai Wang,
• Wenshi Fan,

Vol. 13, No. 2, February 27, 2019
10.3837/tiis.2019.02.020, Download Paper (Free):

• Motion deblurring
• blind deconvolution
• kernel estimation
• adaptive iterative strategy

Abstract

In this paper, an adaptive iterative algorithm is proposed for motion deblurring by using the salient intensity prior. Based on the observation that the salient intensity of the clear image is sparse, and the salient intensity of the blurred image is less sparse during the image blurring process. The salient intensity prior is proposed to enforce the sparsity of the distribution of the saliency in the latent image, which guides the blind deblurring in various scenarios. Furthermore, an adaptive iteration strategy is proposed to adjust the number of iterations by evaluating the performance of the latent image and the similarity of the estimated blur kernel. The negative influence of overabundant iterations in each scale is effectively restrained in this way. Experiments on publicly available image deblurring datasets demonstrate that the proposed algorithm achieves state-of-the-art deblurring results with small computational costs.

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