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Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.


Papers
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Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper propose a co-attention network (CANet) to build sound interaction between RGB and depth features, which includes three modules: position and channel coattention fusion modules adaptively fuse RGB and D features in spatial and channel dimensions.

24 citations

Patent
01 Jun 2015
TL;DR: In this paper, a master image display apparatus acquires determination results obtained by an input detector and acquires a video signal of which channel is selected by a selector from each of a plurality of image display apparatuses.
Abstract: In a multi-screen display apparatus according to the present invention, a master image display apparatus acquires determination results obtained by an input detector and acquires a video signal of which channel is selected by a selector from each of a plurality of image display apparatuses including the master image display apparatus. In a case where the selector of any of the image display apparatuses selects a video signal of a channel that is not input to the video receiver, the master image display apparatus controls the selector of the image display apparatus to select a video signal of another channel that is input to the video receiver and controls the selector of at least one of the others of the image display apparatuses to select a video signal of the other channel.

24 citations

Journal ArticleDOI
TL;DR: A simple yet effective approach using Retinex theory and Taylor series expansion for nighttime image dehazing, referred to as ‘RDT’ is proposed, which demonstrates the superior performance of the proposed RDT method over the state-of-the-art methods.

24 citations

Journal ArticleDOI
TL;DR: Competitive results demonstrate that the RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image can extract more effective features and is more conducive to classifying a scene.
Abstract: The scene classification of a remote sensing image has been widely used in various fields as an important task of understanding the content of a remote sensing image. Specially, a high-resolution remote sensing scene contains rich information and complex content. Considering that the scene content in a remote sensing image is very tight to the spatial relationship characteristics, how to design an effective feature extraction network directly decides the quality of classification by fully mining the spatial information in a high-resolution remote sensing image. In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative networks of CNN, which shows a stronger feature learning ability as it fully utilizes all the convolutional layer information. Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image. First, multi-layer convolutional features are fused with residual dense blocks. Then, a channel-spatial attention module is added to obtain more effective feature representation. Finally, softmax classifier is applied to classify the scene after adopting data augmentation strategy for meeting the training requirements of the network parameters. Five experiments are conducted on the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID), and the competitive results demonstrate that our method can extract more effective features and is more conducive to classifying a scene.

24 citations

Journal ArticleDOI
Fan Guo1, Xin Zhao1, Jin Tang1, Hui Peng1, Lijue Liu1, Beiji Zou1 
TL;DR: A deep convolutional network for single image dehazing based on derived image fusion strategy that performs comparably or even better than state-of-the-art methods in terms of the peak signal-to-noise ratio (PSNR), structure similarity (SSIM) and visual quality.

24 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202216
2021559
2020643
2019696
2018613
2017496