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Scale-wise Convolution for Image Restoration
[article]
2019
arXiv
pre-print
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Inspired from spatial-wise convolution for shift-invariance, "scale-wise convolution" is proposed to convolve across multiple scales for scale-invariance. ...
The proposed network with scale-wise convolution achieves superior performance in multiple image restoration tasks including image super-resolution, image denoising and image compression artifacts removal ...
Figure 2 : 2 Overview of scale-wise convolutional networks for image restoration. ...
arXiv:1912.09028v1
fatcat:oqcamspjefdchgl45reqorn7wq
Scale-Wise Convolution for Image Restoration
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Inspired from spatial-wise convolution for shift-invariance, "scale-wise convolution" is proposed to convolve across multiple scales for scale-invariance. ...
The proposed network with scale-wise convolution achieves superior performance in multiple image restoration tasks including image super-resolution, image denoising and image compression artifacts removal ...
Unified Architecture for Image Restoration. The proposed SCN has multiple cascaded residual blocks and a Figure 2 : Overview of scale-wise convolutional networks for image restoration. ...
doi:10.1609/aaai.v34i07.6706
fatcat:crkggxiwdze4hhec5krnj5pysm
Progressive Multi-Scale Residual Network for Single Image Super-Resolution
[article]
2020
arXiv
pre-print
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Furthermore, channel- and pixel-wise attention mechanism (CPA) is designed for finding the inherent correlations among image features with weighting and bias factors, which concentrates more on high-frequency ...
Multi-scale convolutional neural networks (CNNs) achieve significant success in single image super-resolution (SISR), which considers the comprehensive information from different receptive fields. ...
12] : There is an effective channel-wise attention design in SENet, which has been widely utilized for different image restoration problems. ...
arXiv:2007.09552v3
fatcat:3fgzt5nhj5csvo5756imjwcree
Multilevel Feature Exploration Network for Image Superresolution
2022
Scientific Programming
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Image superresolution (SR) is a classical issue in computer vision area. Recently, there are elaborated convolutional neural networks (CNNs) demonstrating remarkable effectiveness on image SR. ...
In this paper, we find that the hierarchical design can effectively restore the structural information and devise a multilevel feature exploration network for image SR (MFSR). ...
Acknowledgments is research was partially supported by the Program for Liaoning Innovation Talents in University (no. LR2019034) and the Overseas Training Foundation of Liaoning (no. 2019GJWYB015). ...
doi:10.1155/2022/2014627
fatcat:uxdg3zenbfg45bfokcbvdnlari
Context‐wise attention‐guided network for single image deraining
2021
Electronics Letters
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In this paper, we propose a context-wise attention-guided network for single image deraining. ...
Fu et al. [4] decomposed the rainy image into scales and devised parallel sub-networks to predict a corresponding clean pyramid, but the correlated features across scales were not exploited, which impaired ...
For image deraining, correlations between different objects can be gleaned from global information, which is beneficial for restoring the corrupted image content. ...
doi:10.1049/ell2.12391
fatcat:jwenspovwvarfbkfvj4723eta4
Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images
2022
Computers Materials & Continua
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Each FE block consists of five convolution layers and one CA block for weighted skip connection. ...
We used Computer Vision Center-Clinic Database (CVC-ClinicDB) consisting of 612 colonoscopy medical images to evaluate the enhancement of image restoration. ...
Fu et al. have proposed a Deep Convolutional Sparse Coding (DCSC) [13] to exploit multi-scale image features using three different dilated convolutions [25] . ...
doi:10.32604/cmc.2022.020651
fatcat:tbnd2wbm3zg2zeibahhesj5leu
Encoder-Decoder Residual Network for Real Super-Resolution
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Real single image super-resolution is a challenging task to restore lost information and attenuate noise from images mixed unknown degradations complicatedly. ...
However, these existing methods do not perform well for real single image super-resolution. ...
For the encoded features, due to the different feature scales, we apply a coarse-to-fine method to restore high-quality image gradually. ...
doi:10.1109/cvprw.2019.00270
dblp:conf/cvpr/ChengMLZZZ19
fatcat:3onqvkna7vhjzknmavph56rtai
Attention Cube Network for Image Restoration
[article]
2020
arXiv
pre-print
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Recently, deep convolutional neural network (CNN) have been widely used in image restoration and obtained great success. ...
To address these issues, we propose an attention cube network (A-CubeNet) for image restoration for more powerful feature expression and feature correlation learning. ...
We report results
for image super-resolution with scaling factor ×2. ...
arXiv:2009.05907v2
fatcat:keguyfstkfe5tl7lus3tmjr5uu
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
[article]
2016
arXiv
pre-print
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In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. ...
Second, these skip connections pass image details from convolutional layers to de-convolutional layers, which is beneficial in recovering the original image. ...
Observing recent superior performance of DNNs on image processing tasks, we propose a convolutional neural network (CNN)-based framework for image restoration. ...
arXiv:1603.09056v2
fatcat:iusp33lw5vh43l6p4xsuookk6m
Restormer: Efficient Transformer for High-Resolution Image Restoration
[article]
2022
arXiv
pre-print
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Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks. ...
restoration tasks involving high-resolution images. ...
Special thanks to Abdullah Abuolaim and Zhendong Wang for providing the results. ...
arXiv:2111.09881v2
fatcat:zzmue7de3feergl5ciry25r3qm
An Image Deblurring Method Using Improved U-Net Model
2022
Mobile Information Systems
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Therefore, we propose an improved U-Net (U-shaped Convolutional Neural Network) model to restore the blurred images. ...
A residual depth-wise separable convolution is designed, which allows for propagation of detailed information from different layers when compared with standard convolution and a standard residual block ...
[9] advanced a brand-new convolution architecture, which greatly expanded the reception eld. A scale-aware convolutional neural network to restore a clear image was also proposed. Quan et al. ...
doi:10.1155/2022/6394788
fatcat:gl5yrvh7jjej5mhkv4rbzhavvi
BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring
[article]
2022
arXiv
pre-print
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Our BANet utilizes region-based self-attention with multi-kernel strip pooling to disentangle blur patterns of different degrees and with cascaded parallel dilated convolution to aggregate multi-scale ...
Extensive experimental results on the GoPro and HIDE benchmarks demonstrate that the proposed BANet performs favorably against the state-of-the-art in blurred image restoration and can provide deblurred ...
With the disentangled region-wise blurred patterns, it then utilizes cascaded multi-scale dilated convolution to restore blurred features. ...
arXiv:2101.07518v3
fatcat:temckblja5eyxetez3aombwlja
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
[article]
2016
arXiv
pre-print
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In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers. ...
Image restoration, including image denoising, super resolution, inpainting, and so on, is a well-studied problem in computer vision and image processing, as well as a test bed for low-level image modeling ...
For efficiency, we convert the images to gray-scale and resize them to smaller images. ...
arXiv:1606.08921v3
fatcat:nfo6kabc4bbk3psyraqwtgdkge
A Multistage with Multiattention Network for Single Image Dehazing
2022
Scientific Programming
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For single image dehazing, an end-to-end multistage with multiattention network is proposed in this paper. ...
image details. ...
Hazy Image 3×3Conv MAU C Single-scale subnet MAU 3×3Conv + Restored Image encoder-decoder subnet SAM Restored Image C concatenate SAM supervised attention block F input 3×3 Conv , ReLU + 3×3 Conv, PONO ...
doi:10.1155/2022/2056662
fatcat:cnw2h5etlbcojlcpiaixc4vt5u
Attention-Based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Many studies have been conducted so far on image restoration, the problem of restoring a clean image from its distorted version. ...
There are many different types of distortion which affect image quality. Previous studies have focused on single types of distortion, proposing methods for removing them. ...
Figure 4 . 4 Examples of restored images by our method, RL-Restore [48] , and DnCNN [50] .DIV2K dataset containing 800 high-quality, large-scale images. ...
doi:10.1109/cvpr.2019.00925
dblp:conf/cvpr/SuganumaLO19
fatcat:rvmxe3epbzaopistdt6xxvykpu
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