Local Distortion

8 papers with code • 1 benchmarks • 0 datasets

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Most implemented papers

Natural Image Stitching with the Global Similarity Prior

nothinglo/NISwGSP European Conference on Computer Vision 2016

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 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 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 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

DVLP-CMATERJU/RectiNet 20 Jul 2020

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

mabulnaga/placenta-flattening 15 Nov 2021

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

fh2019ustc/doctr-plus 18 Apr 2023

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

chaofengc/iqa-pytorch 6 Aug 2023

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.