• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.27-33
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• 2009

We propose an efficient method for single image motion deblurring using edge prediction. Previous methods for motion deblurring from a single image have been based on total variation or natural image statistics. In contrast, our method predicts sharp edges by applying bilateral and shock filters and manipulating image gradients directly, and estimates motion blur using the predicted sharp edges. Sharp edge prediction makes our method possible to deblur efficiently with less computation. Results show that our method can effectively and efficiently restore images degraded by large complex motion blur.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.645-650
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• 2015

Image restoration is required when the image is blurred due to out of focus or motion during the image acquisition. This type of image restoration is known as ill-posed inverse problem because the estimate of an original image should be derived from only one blurred image. This paper introduces a reference image to facilitate the restoration process. The experimental result shows that computation time is significantly reduced, compared with other methods. The proposed method obtains the estimate of the kernel used in blurring processing. New cost function is defined to update both the image and the kernel alternately. In the last stage, Wiener filter produces the estimate of an original image using the kernel and the reference image.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.484-494
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• 2018

The uniform motion blur removing algorithm of Cho et al. has the problem that the edge region of the image cannot be restored clearly. We propose the effective algorithm to overcome this problem by using shock filter that reconstructs a blurred step signal into a sharp edge, and convolutional neural network (CNN) that learns by extracting features from the image. Then uniform motion blur kernel is estimated from the latent sharp image to remove blur in the image. The proposed algorithm improved the disadvantages of the conventional algorithm by reconstructing the latent sharp image using shock filter and CNN. Through the experimental results, it was confirmed that the proposed algorithm shows excellent reconstruction performance in objective and subjective image quality than the conventional algorithm.