Edge/Structure Preserving Smoothing via Relativity-of-Gaussian

Bolun Cai, Xiaofen Xing, Xiangmin Xu

Introduction

This paper presents a novel edge/structure-preserving image smoothing via relativity-of-Gaussian. As a simple local regularization, it performs the local analysis of scale features and globally optimizes its results into a piecewise smooth. The central idea to ensure proper texture smoothing is based on cross-scale relative that captures the weak textures from the most prominent edges/structures. Our method outperforms the previous methods in removing the detail information while preserving main image content.

If you use these codes in your research, please cite:

@article{cai2017rog,
	author = {Bolun Cai, Xiaofen Xing and Xiangmin Xu},
	title={Edge/Structure Preserving Smoothing via Relativity-of-Gaussian},
	booktitle={IEEE International Conference on Image Processing (ICIP)},
	year={2017}
	}

Usage

Download the code and test images

git clone https://github.com/caibolun/RoG.git

Edge/Structure Preserving Smoothing

Smooth the image with edge/structure Preserving by simply typing in Matlab.

 run('demo.m')

  

Detail Enhancement (Link)

As a nonlinear edge-preserving image smoothing (K = 1), our method can be used for detail enhancement via base and detail layer decomposition.

run('detail_example\demo.m')

  

Structure Extraction (Link)

As a nonlinear structure-preserving image smoothing (K > 1), we apply our method for structure-texture separation.

run('struct_example\demo.m')

  

HDR Tone Mapping (Link)

One of the challenges in image processing is the rendering of an HDR scene on a conventional LDR display. RoG smoothing is easily harnessed to perform tone mapping of HDR images.

run('hdr_example\demo.m')