https://www.selleckchem.com/products/ca3.html
Traditional feature-based image stitching technologies rely heavily on feature detection quality, often failing to stitch images with few features or low resolution. The learning-based image stitching solutions are rarely studied due to the lack of labeled data, making the supervised methods unreliable. To address the above limitations, we propose an unsupervised deep image stitching framework consisting of two stages unsupervised coarse image alignment and unsupervised image reconstruction. In the first stage, we design an ablation-based l
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