Book ChapterDOI
Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network
Sifei Liu,Jinshan Pan,Jinshan Pan,Ming-Hsuan Yang +3 more
- pp 560-576
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TLDR
Experimental results show that many low-level vision tasks can be effectively learned and carried out in real-time by the proposed algorithm and is significantly smaller and faster in comparison with a deep CNN based image filter.Abstract:
In this paper, we consider numerous low-level vision problems (e.g., edge-preserving filtering and denoising) as recursive image filtering via a hybrid neural network. The network contains several spatially variant recurrent neural networks (RNN) as equivalents of a group of distinct recursive filters for each pixel, and a deep convolutional neural network (CNN) that learns the weights of RNNs. The deep CNN can learn regulations of recurrent propagation for various tasks and effectively guides recurrent propagation over an entire image. The proposed model does not need a large number of convolutional channels nor big kernels to learn features for low-level vision filters. It is significantly smaller and faster in comparison with a deep CNN based image filter. Experimental results show that many low-level vision tasks can be effectively learned and carried out in real-time by the proposed algorithm.read more
Citations
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Proceedings ArticleDOI
Semantic Image Inpainting with Deep Generative Models
Raymond A. Yeh,Chen Chen,Teck Yian Lim,Alexander G. Schwing,Alexander G. Schwing,Mark Hasegawa-Johnson,Minh N. Do +6 more
TL;DR: A novel method for semantic image inpainting, which generates the missing content by conditioning on the available data, and successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.
Journal ArticleDOI
Deep bilateral learning for real-time image enhancement
TL;DR: In this paper, a convolutional neural network is used to predict the coefficients of a locally affine model in bilateral space, which is then applied to the full-resolution image.
Proceedings ArticleDOI
Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks
TL;DR: Quantitative and qualitative evaluations on public datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of accuracy, speed, and model size.
Journal ArticleDOI
Edge-Enhanced GAN for Remote Sensing Image Superresolution
TL;DR: A generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise is proposed.
Journal ArticleDOI
Low-Light Image Enhancement via a Deep Hybrid Network
TL;DR: A novel spatially variant recurrent neural network (RNN) is proposed as an edge stream to model edge details, with the guidance of another auto-encoder, to enhance the visibility of degraded images.
References
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Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Proceedings ArticleDOI
Bilateral filtering for gray and color images
Carlo Tomasi,Roberto Manduchi +1 more
TL;DR: In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception.
Journal ArticleDOI
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
TL;DR: An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Journal ArticleDOI
Guided Image Filtering
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TL;DR: The guided filter is a novel explicit image filter derived from a local linear model that can be used as an edge-preserving smoothing operator like the popular bilateral filter, but it has better behaviors near edges.