An image that has been subject to the out-of-focus phenomenon has reduced sharpness, contrast and level of detail depending on the amount of defocus. To restore out-of-focused images is a complex task due to the information loss that occurs. However there exist many restoration algorithms that attempt to revert this defocus by estimating a noise model and utilizing the point spread function.
The purpose of this thesis, proposed by FLIR Systems, was to ﬁnd a robust algorithm that can restore focus and from the customer’s perspective be user friendly. The thesis includes three implemented algorithms that have been com-pared to MATLABs built-in. Three image series were used to evaluate the limits and performance of each algorithm, based on deblurring quality, implementation complexity, computation time and usability.
Results show that the Alternating Direction Method for total variation de-convolution proposed by Tao et al. together with its the modiﬁed discrete cosines transform version restores the defocused images with the highest quality. These two algorithms include features such as, fast computational time, few parameters to tune and a powerful noise reduction.
Source: Linköping University
Author: Zhu, Peter