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Article summary:

1. This paper proposes a learning framework for image stitching which consists of a large-baseline deep homography module and an edge-preserved deformation module.

2. The proposed learning framework can stitch images of arbitrary views and input sizes, thus contributing to a supervised deep image stitching method with excellent generalization capability in other real images.

3. Experimental results demonstrate that the proposed homography module significantly outperforms existing deep homography methods in large baseline scenes, and is superior to existing learning methods in image stitching.

Article analysis:

The article is generally reliable and trustworthy as it provides detailed information about the proposed learning framework for image stitching, its components, and the experimental results demonstrating its effectiveness compared to existing methods. The authors also provide evidence for their claims by citing relevant literature and providing experimental results from their own experiments.

However, there are some potential biases that should be noted. For example, the authors do not explore any counterarguments or alternative approaches to image stitching that may be more effective than their proposed method. Additionally, they do not discuss any possible risks associated with using their method or any potential drawbacks that could arise from its use. Furthermore, the article does not present both sides of the argument equally; instead it focuses solely on promoting the benefits of their proposed approach without considering any potential drawbacks or limitations.