8 papers with code • 1 benchmarks • 0 datasets
In this work, we propose DewarpNet, a deep-learning approach for document image unwarping from a single image.
While most prior work focuses on synthetic input shapes, our formulation is designed to be applicable to real-world scans with imperfect input correspondences and various types of noise.
However, due to the curved and highly variable in vivo shape of the placenta, interpreting and visualizing these images is difficult.
Our approach to IQA involves the design of a heuristic coarse-to-fine network (CFANet) that leverages multi-scale features and progressively propagates multi-level semantic information to low-level representations in a top-down manner.