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Denoised Non-Local Neural Network for Semantic Segmentation [article]

Qi Song, Jie Li, Hao Guo, Rui Huang
2021 arXiv   pre-print
The non-local network has become a widely used technique for semantic segmentation, which computes an attention map to measure the relationships of each pixel pair.  ...  Specifically, we inventively propose a Denoised Non-Local Network (Denoised NL), which consists of two primary modules, i.e., the Global Rectifying (GR) block and the Local Retention (LR) block, to eliminate  ...  INTRODUCTION R ECENTLY, non-local self-attention mechanisms [1]- [7] are widely utilized in semantic segmentation to capture long-range dependencies.  ... 
arXiv:2110.14200v1 fatcat:zdmmlkx6bre53fwco2yka5xhjm

Deeply Cascaded U-Net for Multi-Task Image Processing [article]

Ilja Gubins, Remco C. Veltkamp
2020 arXiv   pre-print
In this paper, we propose a novel multi-task neural network architecture designed for combining sequential image processing tasks.  ...  We demonstrate effectiveness of the proposed approach on denoising and semantic segmentation, as well as on progressive coarse-to-fine semantic segmentation, and achieve better performance than multiple  ...  However, even if tasks are processed sequentially, it is a common practice to use separate models for each problem, first one neural network for denoising, and then a second for segmentation of the previously  ... 
arXiv:2005.00225v1 fatcat:k7azv6igxfaofm5iltzihb635a

Connecting Image Denoising and High-Level Vision Tasks via Deep Learning [article]

Ding Liu, Bihan Wen, Jianbo Jiao, Xianming Liu, Zhangyang Wang, Thomas S. Huang
2018 arXiv   pre-print
Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network  ...  First for image denoising we propose a convolutional neural network in which convolutions are conducted in various spatial resolutions via downsampling and upsampling operations in order to fuse and exploit  ...  Non-local self-similarity of images is exploited and incorporated into a recurrent neural network in [35] . III.  ... 
arXiv:1809.01826v1 fatcat:fikd6rjy6zai7fekuktwlp2k3e

When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, Thomas Huang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network  ...  First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance.  ...  Classical image denoising methods take advantage of local or non-local structures presented in the image [Aharon et al., 2006; Dabov et al., 2007b; Mairal et al., 2009; Dong et al., 2013; Gu et al., 2014  ... 
doi:10.24963/ijcai.2018/117 dblp:conf/ijcai/LiuWLWH18 fatcat:bxinvguvz5cm3dn2gdwnl5lr64

When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach [article]

Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, Thomas S. Huang
2018 arXiv   pre-print
Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network  ...  First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance.  ...  Classical image denoising methods take advantage of local or non-local structures presented in the image [Aharon et al., 2006; Dabov et al., 2007b; Mairal et al., 2009; Dong et al., 2013; Gu et al., 2014  ... 
arXiv:1706.04284v3 fatcat:6ox62664n5hzvne345hwpb7use

Front Matter: Volume 11878

Xudong Jiang, Hiroshi Fujita
2021 Thirteenth International Conference on Digital Image Processing (ICDIP 2021)  
Publication of record for individual papers is online in the SPIE Digital Library.  ...  11878 0N LightSeg: a light-weight network for real-time semantic segmentation [11878-58] 11878 0O Class-related graph convolution for weakly supervised semantic segmentation [11878-59] 11878 0P  ...  IMAGE DENOISING AND DIGITAL WATERMARKING 0Z Convolutional neural network combined with wavelet denoising for multi-category analysis on heart sound 11878 10 A tampering detection algorithm based on  ... 
doi:10.1117/12.2603859 fatcat:7iznff73tze2fdyoz2qy6ookpu

BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation [article]

Mo Zhang, Fei Yu, Jie Zhao, Li Zhang, Quanzheng Li
2021 arXiv   pre-print
To tackle this issue, we propose Boundary Enhancement and Feature Denoising (BEFD) module to facilitate the network ability of extracting boundary information in semantic segmentation, which can be integrated  ...  By introducing Sobel edge detector, the network is able to acquire additional edge prior, thus enhancing boundary in an unsupervised manner for medical image segmentation.  ...  Following the idea of non-local means [4] and non-local neural networks [21] , the work [23] presented a denoising block to make feature denoising.  ... 
arXiv:2104.03768v1 fatcat:vbphzuxw4fd2fmravmv4xc5guy

Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting [article]

Shunxin Xu, Ke Sun, Dong Liu, Zhiwei Xiong, Zheng-Jun Zha
2021 arXiv   pre-print
The proposed network is composed of multiple segmentation and denoising blocks (SDBs), each of which estimates semantic map then uses the map to regularize denoising.  ...  We then propose a boosting network to perform denoising and segmentation alternately.  ...  Thus, given a semantic segmentation map, the image content similarity may be better identified, and the non-local correlation may be better exploited.  ... 
arXiv:2102.12095v1 fatcat:fbnz55d24ncydod7yaclmrm7sm

Height Prediction and Refinement From Aerial Images With Semantic and Geometric Guidance

Mahdi Elhousni, Ziming Zhang, Xinming Huang
2021 IEEE Access  
This manuscript proposes a two-stage approach to solve this task, where the first stage is a multi-task neural network whose main branch is used to predict the height map resulting from a single RGB aerial  ...  input image, while being augmented with semantic and geometric information from two additional branches.  ...  In addition, we compare our deep learning based denoiser with other popular non-learning denoising algorithms such as Bilateral Filtering (BF) [27] and Non-local Means (NIM) regularization [29] .  ... 
doi:10.1109/access.2021.3122894 fatcat:puxkwtiyj5he5ahp2ozwddj5cm

Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric Guidance [article]

Elhousni Mahdi, Zhang Ziming, Huang Xinming
2021 arXiv   pre-print
This letter proposes a two-stage approach, where first a multi-task neural network is used to predict the height map resulting from a single RGB aerial input image.  ...  We also include a second refinement step, where a denoising autoencoder is used to produce higher quality height maps.  ...  In addition, we compare our deep learning based denoiser with other popular non-learning denoising algorithms such as Bilateral Filtering (BF) [27] and Non-local Means (NIM) regularization [29] .  ... 
arXiv:2011.10697v4 fatcat:rz5zwjro35divbfoew6ek2pmuy

Deep Class Aware Denoising [article]

Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein
2017 arXiv   pre-print
We further show that a significant boost in performance of up to 0.4 dB PSNR can be achieved by making our network class-aware, namely, by fine-tuning it for images belonging to a specific semantic class  ...  The increasing demand for high image quality in mobile devices brings forth the need for better computational enhancement techniques, and image denoising in particular.  ...  The first neural network to achieve state-of-the-art performance in image denoising has been proposed in [10] .  ... 
arXiv:1701.01698v2 fatcat:hmfj2n3tmfbfngtbjcqtgsj6tm

Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder [article]

Jeffrey M. Ede
2018 arXiv   pre-print
Its performance is benchmarked against bilateral, non-local means, total variation, wavelet, Wiener and other restoration methods with their default parameters.  ...  and then fine-tuned for ordinary doses (200-2500 counts ppx).  ...  NL -non-local.  ... 
arXiv:1807.11234v2 fatcat:ijslzpsvsjbilazrzvkroj7fry

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 641-656 Deep Non-Local Kalman Network for Video Compression Artifact Reduction.  ...  Zhou, H., +, TIP 2020 5216-5228 Deep Non-Local Kalman Network for Video Compression Artifact Reduc- tion.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting

Shunxin Xu, Ke Sun, Dong Liu, Zhiwei Xiong, Zheng-Jun Zha
2022 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
The proposed network is composed of multiple segmentation and denoising blocks (SDBs), each of which estimates a semantic map then uses the map to regularize denoising.  ...  We then propose a boosting network to perform denoising and segmentation alternately.  ...  ACKNOWLEDGMENTS This work was supported by the Natural Science Foundation of China under Grants 62022075 and 62021001, and by the Fundamental Research Funds for the Central Universities under Grant WK3490000006  ... 
doi:10.1145/3548459 fatcat:qdmxftcgbvh7hlmw5os2m7j44u

All One Needs to Know about Priors for Deep Image Restoration and Enhancement: A Survey [article]

Yunfan Lu, Yiqi Lin, Hao Wu, Yunhao Luo, Xu Zheng, Lin Wang
2022 arXiv   pre-print
Due to its ill-posed property, plenty of works have explored priors to facilitate training deep neural networks (DNNs).  ...  Therefore, this paper serves as the first study that provides a comprehensive overview of recent advancements of priors for deep image restoration and enhancement.  ...  For example, Liu et al. [138] used the prior for denoising to improve the performance of deep semantic segmentation models.  ... 
arXiv:2206.02070v1 fatcat:icu7hwua3jggbp7owl2l5mgyfu
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