computer-vision-1

image smoothing

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in image-smoothing-1

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find image-smoothing-1 models and implementations

Datasets

Real world moire pattern classification

Subtasks

No subtasks available.

Most implemented papers

Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

Dmytro Mishkin, Edgar Riba, D. Ponsa, Ethan Rublee, Gary R. Bradski•Fri Oct 04 2019

Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations.

410

Content

0

Paper Graph

A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing

Jiaolong Yang, G. Hua, Baoquan Chen, Qingnan Fan, D. Wipf•Thu Aug 10 2017

A deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering by estimating edges and reconstructing images using only cascaded convolutional layers arranged such that no handcrafted or application-specific image-processing components are required.

309 0

Paper Graph

Decouple Learning for Parameterized Image Operators

Lu Yuan, Dongdong Chen, G. Hua, Baoquan Chen, Nenghai Yu, Qingnan Fan•Fri Jul 20 2018

A new decouple learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network is proposed.

65 0

Paper Graph

Image smoothing via unsupervised learning

Jiaolong Yang, Xin Tong, Baoquan Chen, Qingnan Fan, D. Wipf•Tue Nov 06 2018

A unified unsupervised (label-free) learning framework that facilitates generating flexible and high-quality smoothing effects by directly learning from data using deep convolutional neural networks (CNNs).

115 0

Paper Graph

A Benchmark for Edge-Preserving Image Smoothing

Feida Zhu, Lei Zhang, Yizhou Yu, Zhetong Liang, Xixi Jia•Sun Mar 31 2019

A benchmark for edge-preserving image smoothing is proposed that is faster than most of the state-of-the-art smoothing algorithms with leading smoothing results both qualitatively and quantitatively.

75 0

Paper Graph

Side Window Filtering

Hui Yin, Yuanhao Gong, G. Qiu•Thu May 16 2019

This work proposes a new Side Window Filtering (SWF) technique which aligns the window's side or corner with the pixel being processed and demonstrates that implementing the SWF principle can effectively prevent artifacts such as color leakage associated with the conventional implementation.

139 0

Paper Graph

A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing

Pingping Zhang, Yinjie Lei, Wei Liu, Xiaolin Huang, Jie Yang, M. Ng•Mon Jul 22 2019

A generalized framework capable of a range of applications and able to outperform the state-of-the-art approaches in several tasks, such as image detail enhancement, clip-art compression artifacts removal, guided depth map restoration, image texture removal, etc.

124 0

Paper Graph

Image Smoothing Algorithm Based on Gradient Analysis

V. Gudkov, I. Moiseev•Thu Apr 30 2020

In this paper image smoothing algorithm based on gradient analysis is proposed. Our algorithm uses filtering and to achieve edge-preserving smoothing it uses two components of gradient vectors: their magnitudes (or lengths) and directions. Our method discriminates between two types of boundaries in given neighborhood: regular and irregular ones. Regular boundaries have small deviations of gradient angles and the opposite for irregular ones. To measure closeness of angles cosine of doubled difference is used. As additional measure that helps to discriminate the types of boundaries inverted gradient values were used. When gradient magnitudes are inverted bigger values refer to textures (insignificant changes in gradient) and smaller refer to strong boundaries. So textures would have bigger weights and hence they would appear smoother. We also propose to filter image of gradient magnitudes with median filter to enhance visual quality of results. The method proposed in this paper is easy to implement and compute and it gives good results in comparison with other techniques like bilateral filter.

0 0

Paper Graph

Concurrently Extrapolating and Interpolating Networks for Continuous Model Generation

H. Bai, Yao Zhao, Lijun Zhao, Jinjing Zhang, Fan Zhang, Anhong Wang•Sat Jan 11 2020

This work generalizes continuous network interpolation as a more powerful model generation tool, and proposes a simple yet effective model generation strategy to form a sequence of models that only requires a set of specific-effect label images.

0 0

Paper Graph

Adding a benchmark result helps the community track progress.