PROJECT TITLE :
Reconstruction of Single Image from Multiple Blurry Measured Images - 2018
The matter of blind image recovery using multiple blurry pictures of the identical scene is addressed during this Project. To perform blind deconvolution, which is additionally called blind image recovery, the blur kernel and image are represented by groups of sparse domains to use the local and nonlocal info such that a novel joint deblurring approach is conceived. Within the proposed approach, the cluster sparse regularization on both the blur kernel and image is provided, where the sparse solution is promoted by l one -norm. Moreover, the reweighted knowledge fidelity is developed to more improve the recovery performance, where the burden is determined by the estimation error. Moreover, to reduce the undesirable noise effects in cluster sparse representation, distance measures are studied within the block matching process to find similar patches. In such a joint deblurring approach, a more refined 2-step interactive method is needed in that every step is solved by suggests that of the well-known split Bregman iteration algorithm, that is mostly used to efficiently solve the proposed joint deblurring drawback. Finally, numerical studies, together with synthetic and real pictures, demonstrate that the performance of this joint estimation algorithm is superior to the previous state-of-the-art algorithms in terms of both objective and subjective evaluation standards. The recovery results of real captured images using unmanned aerial vehicles are provided to more validate the effectiveness of the proposed methodology.
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