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Guided filter-based blind image restoration method
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As a major branch and research focus in the image processing field, the study on image restoration is always of practical significance and application value. Blind image restoration has always been one of the most difficult problems in image restoration. In view of the image motion blurring induced by the relative motion between the camera and the subject, we present a blind image restoration method based on guided filter. We first estimate the point spread function by using the iteration algorithm in the frequency domain. And then, because the guided filter has the edge-preserving smoothing property, we restore the target image by the non-blind image restoration algorithm based on the guided filter. By iterating the above two steps, we can obtain the original clear image. In order to verify the effectiveness of the proposed algorithm, we give several groups of experiments. Experimental results show that the proposed algorithm can not only effectively eliminate the noise and suppress the ringing, but also well preserve the edge and texture details. Therefore, the proposed algorithm can restore the image with high quality.
Acta Physica Sinica, Chinese Physical Society and Institute of Physics, Chinese Academy of Sciences
Title: Guided filter-based blind image restoration method
Description:
As a major branch and research focus in the image processing field, the study on image restoration is always of practical significance and application value.
Blind image restoration has always been one of the most difficult problems in image restoration.
In view of the image motion blurring induced by the relative motion between the camera and the subject, we present a blind image restoration method based on guided filter.
We first estimate the point spread function by using the iteration algorithm in the frequency domain.
And then, because the guided filter has the edge-preserving smoothing property, we restore the target image by the non-blind image restoration algorithm based on the guided filter.
By iterating the above two steps, we can obtain the original clear image.
In order to verify the effectiveness of the proposed algorithm, we give several groups of experiments.
Experimental results show that the proposed algorithm can not only effectively eliminate the noise and suppress the ringing, but also well preserve the edge and texture details.
Therefore, the proposed algorithm can restore the image with high quality.
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