Williams BM, Zhang J, Chen K. A new image deconvolution method with fractional regularisation. Journal of Algorithms & Computational Technology. 2016 Dec;10(4):265-76.
https://journals.sagepub.com/doi/pdf/10.1177/1748301816660439
Abstract
Image deconvolution is an important pre-processing step in image analysis which may be combined with denoising, also
an important image restoration technique, and prepares the image to facilitate diagnosis in the case of medical images and
further processing such as segmentation and registration. Considering the variational approach to this problem, regularisation is a vital component for reconstructing meaningful information and the problem of defining appropriate regularisation is an active research area. An important question in image deconvolution is how to obtain a restored image
which has sharp edges where required but also allows smooth regions. Many of the existing regularisation methods allow
for one or the other but struggle to obtain good results with both. Consequently, there has been much work in the area
of variational image reconstruction in finding regularisation techniques which can provide good quality restoration for
images which have both smooth regions and sharp edges. In this paper, we propose a new regularisation technique for
image reconstruction in the blind and non-blind deconvolution problems where the precise cause of blur may or may not
be known. We present experimental results which demonstrate that this method of regularisation is beneficial for
restoring images and blur functions which contain both jumps in intensity and smooth regions.
