Synergy between Semantic Segmentation and Image Denoising via Alternate Boosting

Author:

Xu Shunxin1ORCID,Sun Ke1ORCID,Liu Dong1ORCID,Xiong Zhiwei1ORCID,Zha Zheng-Jun1ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, Anhui, China

Abstract

The capability of image semantic segmentation may be deteriorated due to the noisy input image, where image denoising prior to segmentation may help. Both image denoising and semantic segmentation have been developed significantly with the advance of deep learning. In this work, we are interested in the synergy between these two tasks by using a holistic deep model. We observe that not only denoising helps combat the drop of segmentation accuracy due to the noisy input, but also pixel-wise semantic information boosts the capability of denoising. We then propose a boosting network to perform denoising and segmentation alternately. The proposed network is composed of multiple segmentation and denoising blocks (SDBs), each of which estimates a semantic map and then uses the map to regularize denoising. Experimental results show that the denoised image quality is improved substantially and the segmentation accuracy is improved to close to that on clean images, and segmentation and denoising are both boosted as the number of SDBs increases. On the Cityscapes dataset, using three SDBs improves the denoising quality to 34.42 dB in PSNR, and the segmentation accuracy to 66.5 in mIoU, when the additive white Gaussian noise level is 50.

Funder

Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

Reference76 articles.

1. Abdelrahman Abdelhamed, Stephen Lin, and Michael S. Brown. 2018. A high-quality denoising dataset for smartphone cameras. In CVPR. IEEE, 1692–1700.

2. Unsupervised hyperspectral stimulated Raman microscopy image enhancement: Denoising and segmentation via one-shot deep learning;Abdolghader Pedram;arXiv preprint arXiv:2104.08338,2021

3. Saeed Anwar and Nick Barnes. 2019. Real image denoising with feature attention. In ICCV. IEEE, 3155–3164.

4. Category-Specific Object Image Denoising

5. Contour Detection and Hierarchical Image Segmentation

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Bridging the Domain Gap in Scene Flow Estimation via Hierarchical Smoothness Refinement;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-06-12

2. Efficient Brain Tumor Segmentation with Lightweight Separable Spatial Convolutional Network;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-05-16

3. Multi-Content Interaction Network for Few-Shot Segmentation;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-03-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3