Local Distortion
8 papers with code • 1 benchmarks • 0 datasets
Most implemented papers
Natural Image Stitching with the Global Similarity Prior
An objective function is designed for specifying the desired characteristics of the warps.
DocUNet: Document Image Unwarping via a Stacked U-Net
teresasun/docUnet.pytorch
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CVPR 2018
The network is trained on this dataset with various data augmentations to improve its generalization ability.
DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks
cvlab-stonybrook/DewarpNet
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ICCV 2019
In this work, we propose DewarpNet, a deep-learning approach for document image unwarping from a single image.
Hamiltonian Dynamics for Real-World Shape Interpolation
marvin-eisenberger/hamiltonian-interpolation
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ECCV 2020
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.
A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping
Capturing images of documents is one of the easiest and most used methods of recording them.
Volumetric Parameterization of the Placenta to a Flattened Template
However, due to the curved and highly variable in vivo shape of the placenta, interpreting and visualizing these images is difficult.
Deep Unrestricted Document Image Rectification
To our best knowledge, this is the first learning-based method for the rectification of unrestricted document images.
TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
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.
