Adaptive recovery of motion blur point spread function from differently exposed images
2010
Motion due to digital camera movement during the image capture process is a major factor that degrades the quality of
images and many methods for camera motion removal have been developed. Central to all techniques is the correct
recovery of what is known as the Point Spread Function (PSF). A very popular technique to estimate the PSF relies on
using a pair of gyroscopic sensors to measure the hand motion. However, the errors caused either by the loss of the
translational component of the movement or due to the lack of precision in gyro-sensors measurements impede the
achievement of a good quality restored image. In order to compensate for this, we propose a method that begins with an
estimation of the PSF obtained from 2 gyro sensors and uses a pair of under-exposed image together with the blurred
image to adaptively improve it.
The luminance of the under-exposed image is equalized with that of the blurred image. An initial estimation of the PSF
is generated from the output signal of 2 gyro sensors. The PSF coefficients are updated using 2D-Least Mean Square
(LMS) algorithms with a coarse-to-fine approach on a grid of points selected from both images.
This refined PSF is used to process the blurred image using known deblurring methods. Our results show that the
proposed method leads to superior PSF support and coefficient estimation. Also the quality of the restored image is
improved compared to 2 gyro only approach or to blind image de-convolution results.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
6
References
5
Citations
NaN
KQI