Deep learning on image denoising: An overview.
TLDR
A comparative study of deep techniques in image denoising by classifying the deep convolutional neural networks for additive white noisy images, the deep CNNs for real noisy images; the deepCNNs for blind Denoising and the deep network for hybrid noisy images.About:
This article is published in Neural Networks.The article was published on 2020-11-01 and is currently open access. It has received 518 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Proceedings ArticleDOI
Multi-Stage Progressive Image Restoration
Syed Waqas Zamir,Aditya Arora,Salman Khan,Munawar Hayat,Fahad Shahbaz Khan,Ming-Hsuan Yang,Ling Shao +6 more
TL;DR: MPRNet as discussed by the authors proposes a multi-stage architecture that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps, and introduces a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Proceedings ArticleDOI
Restormer: Efficient Transformer for High-Resolution Image Restoration
TL;DR: Restormer as discussed by the authors proposes an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images.
Posted Content
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
TL;DR: Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects and a novel IoU constant factor is added to the smooth L1 loss to address the long standing boundary problem.
Journal ArticleDOI
Coarse-to-Fine CNN for Image Super-Resolution
TL;DR: A coarse-to-fine SR CNN (CFSRCNN) to recover a high-resolution (HR) image from its low-resolution version, and demonstrates the high efficiency and good performance of the model on benchmark datasets compared with state-of-the-art SR models.
Journal ArticleDOI
Artificial Intelligence in the Creative Industries: A Review
TL;DR: It is concluded that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.