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Several papers have reported motion deblurring methods that consider the camera's egomotion while the shutter is open, which is estimated by using gyro sensors and accelerometers [9] or the camera's geometric location [10].
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Although such a geometrically motivated model can well approximate camera motion poses, deblurring methods in this line usually suffer from the problems of heavy computational demanding or extensive memory cost.
As compared with methods presented in related papers on motion deblurring and image stabilization, our frame-by-frame intermittent tracking method has the following advantages.
c Motion deblurring (MD).
Compared with uniform deblurring methods, GAC method also can achieve satisfactory deblurring results.
These approaches include single-image deblurring methods [3 5] and multi-image deblurring methods [6 8].
Fig. 11 Deblurring effect comparison with uniform deblurring methods on uniformly blurred images.
Experimental results show that GAC-based deblurring methods can obtain satisfactory deblurring results compared to both state-of-the-art uniform and non-uniform deblurring methods and are much more efficient than non-uniform deblurring methods.
Therefore, they conclude that linear motion deblurring is applicable for mobile devices [3].
The proposed framework is more general and can be easily extended to existing deblurring methods.
Motion deblurring is a frequently used approach for reducing this image degradation resulting from motion blur in image shooting.
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