Maximum Entropy Principle in Image Restoration
Many imaging systems are faced with the problem of estimating a true image from a degraded dataset. In such systems, the image degradation is translated into a convolution with a Point Spread Function (PSF) and addition of noise. Often, the image recovery by inverse filtering is not possible becau...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2018-05-01
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| Series: | Advances in Electrical and Computer Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2018.02010 |
| Summary: | Many imaging systems are faced with the problem of estimating a true image from a degraded dataset.
In such systems, the image degradation is translated into a convolution with a Point Spread Function
(PSF) and addition of noise. Often, the image recovery by inverse filtering is not possible because
the PSF matrix is ill-conditioned. Maximum Entropy (MaxEnt) is an alternative method, which uses the
entropy concept for estimating the true image. This paper presents MaxEnt method, starting with the
historical references of the entropy concept and finalizing with its application in image restoration
and reconstruction. The statistical model of MaxEnt for images is discussed and the connection of
MaxEnt with the Bayesian inference is explained. MaxEnt is evaluated by using a modified version
of Cornwell algorithm. Two cases are considered: images degraded by various PSF kernels in presence
of additive noise and images resulted from incomplete datasets. The tests show PSNR gains ranging
from 1 to 7dB for the degraded images and images reconstructed at 25dB from datasets with up to 80%
missing pixels. |
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| ISSN: | 1582-7445 1844-7600 |