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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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Proceedings ArticleDOI
07 May 1996
TL;DR: In this article, the asymptotic properties of a subspace method using this orthogonality property is presented, and an asymPTotically correct weighting matrix is derived, demonstrating an attainable lower theoretical bound using the subspace estimate.
Abstract: This paper considers the problem of blind channel estimation of multi-channel FIR filters. This is a problem arising in, for example, mobile communication systems using digital signalling. By using the orthogonality property between the noise subspace and the channel matrix, it has been shown in earlier work that the channel matrix is identifiable up to a multiplicative constant. In this article, the asymptotic properties of a subspace method using this orthogonality property is presented. An asymptotically correct weighting matrix is derived, demonstrating an attainable lower theoretical bound using the subspace estimate.

21 citations

Journal ArticleDOI
TL;DR: An efficient block-based steganographic method for halftone images is proposed, based on optimal dispersion degree (DD), which can measure the complexity of the region texture and choose blocks with complex texture to reduce the visual distortion.
Abstract: Halftone images are usually used in facsimile and halftone image steganography can be used for facsimile channel In recent years, real-time image processing becomes more and more important In this paper, an efficient block-based steganographic method for halftone images is proposed This method is based on optimal dispersion degree (DD), which can measure the complexity of the region texture To reduce the visual distortion, the blocks with complex texture can be selected as carriers according to the dispersion degree Finally, the secret messages are embedded by flipping the pixels that can minimize the changes of texture structure The experiments demonstrate that the proposed scheme maintains a good image visual quality and realizes acceptable statistical security with high capacity

21 citations

Journal ArticleDOI
TL;DR: The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image and better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate.
Abstract: Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.

21 citations

Journal ArticleDOI
TL;DR: In this article, a hazy image is divided into blocks of size 32 × 32, and the score of each block is calculated to select a block having the highest score, then the radiance is computed using a transmission map and atmospheric light.

21 citations

Patent
30 Mar 2006
TL;DR: In this article, a sound image localizer consisting of an input section 4, a parameter calculator 5, and a signal processor 6 is presented to represent sound image localization of a video image in a content accurately.
Abstract: PROBLEM TO BE SOLVED: To represent sound image localization of a video image in a content accurately in a device for reproducing the sound field of sound synchronized with the video image, specifically an object in the video image, by outputting to a speaker array. SOLUTION: A sound image localizer comprises an input section 4, a parameter calculator 5, and a signal processor 6. The input section 4 receives a sound signal 41 which accompanies one or more objects appearing in a video image, and the positional information 42 in the video image of the object synchronized with the video image for every object. The signal processor 6 processes the input signal of that sound, and outputs a digital sound signal to each channel of a plurality of speakers. Based on the positional information of the object, a virtual sound source is set in front of a display for displaying the video image, and various parameters 51, 52 and 53 for forming the sound field of the virtual sound source are calculated for every channel. The signal processor 6 outputs the digital sound signal to each channel of each speaker based on the various parameters. COPYRIGHT: (C)2008,JPO&INPIT

21 citations


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