Abstract
The multichannel blind image deconvolution (MBID) problem is how to recover a single true (original) image from multiple blurred images without resorting to any prior knowledge about the true image and the blur functions (except for some general assumptions). In this thesis, we employ a sliding window which shifts over the whole blurred images for data rearrangement to formulate the MBID problem as a multi-input multi-output (MIMO) problem, where the multiple inputs correspond to different sub-images of the true image. By convex analysis and the relationship among these sub-images, we propose a convex analysis based MBID (CAMBID) criterion, and develop an algorithm that fulfills the criterion by the least squares solution. We show the true image identifiability of the CAMBID criterion in the absence of noise. Some simulation results are presented to demonstrate that our proposed algorithm provides better performance for higher SNRs and less computation time than several existing benchmark algorithms.