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A new image deconvolution method with fractional regularisation

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>28/07/2016
<mark>Journal</mark>Journal of Algorithms and Computational Technology
Issue number4
Number of pages12
Pages (from-to)265-276
Publication StatusPublished
<mark>Original language</mark>English


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, regularization is a vital component for reconstructing meaningful information and the problem of defining appropriate regularization 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.