Big Snapshot Stitching with Scarce Overlap
We address certain properties that arise in gigapixel-scale image stitching for snapshot images captured with a novel micro-camera array system, AWARE-2. This system features a greatly extended field of view and high optical resolution, offering unique sensing capabilities for a host of important applications. However, three simultaneously arising conditions pose a challenge to existing approaches to image stitching, with regard to the quality of the output image as well as the automation and efficiency of the image composition process. Put simply, they may be described as the sparse, geometrically irregular, and noisy (S.I.N.) overlap amongst the fields of view of the constituent micro-cameras. We introduce a computational pipeline for image stitching under these conditions, which is scalable in terms of complexity and efficiency. With it, we also substantially reduce or eliminate ghosting effects due to misalignment factors, without entailing manual intervention. Our present implementation of the pipeline leverages the combined use of multicore and GPU architectures. We present experimental results with the pipeline on real image data acquired with AWARE-2.