A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Denoised Non-Local Neural Network for Semantic Segmentation
[article]
2021
arXiv
pre-print
Preserved Fulltext
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2021
Thirteenth International Conference on Digital Image Processing (ICDIP 2021)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
., +, 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
2022
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 3,722 results