Topic
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.
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29 Mar 1983
TL;DR: For displaying spoken words as color pictures on a screen, each audio frequency, i.e., each acoustic sound, is assigned a respective color hue and each audio spectrum spectrum a corresponding color mixture, by conducting acoustic signals through a three-channel triangular filter, each channel having a different central frequency, and controlling the intensity of a respective electron beam of a color television monitor as a function of each filter channel output.
Abstract: For displaying spoken words as color pictures on a screen, each audio frequency, ie each acoustic sound, is assigned a respective color hue and each audio frequency spectrum a respective color mixture, by conducting acoustic signals through a three-channel triangular filter, each channel having a different central frequency, and controlling the intensity of a respective electron beam of a color television monitor as a function of a respective filter channel output
47 citations
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TL;DR: Experiments demonstrate that the proposed halo-reduced dark channel prior dehazing method for sand dust image enhancement can well remove the overall yellowing tone and dust haze effect and obtain normal visual colors and a detailed clear image.
Abstract: The images that are captured in sand storms often suffer from low contrast and serious color cast that are caused by sand dust, and these issues will have significant negative effects on the performance of an outdoor computer vision system. To address these problems, a method based on halo-reduced dark channel prior (DCP) dehazing for sand dust image enhancement is proposed in this paper. It includes three components in sequence: color correction in the LAB color space based on gray world theory, dust removal using a halo-reduced DCP dehazing method, and contrast stretching in the LAB color space using a Gamma function improved contrast limited adaptive histogram equalization (CLAHE), in which a guided filter is used to improve the artifacts of the histogram equalization. Experiments on a large number of real sand dust images demonstrate that the proposed method can well remove the overall yellowing tone and dust haze effect and obtain normal visual colors and a detailed clear image.
47 citations
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TL;DR: A constrained least-squares multichannel image restoration approach is proposed, in which no prior knowledge of the noise variance at each channel or the degree of smoothness of the original image is required.
Abstract: In this correspondence, a constrained least-squares multichannel image restoration approach is proposed, in which no prior knowledge of the noise variance at each channel or the degree of smoothness of the original image is required. The regularization functional for each channel is determined by incorporating both within-channel and cross-channel information. It is shown that the proposed smoothing functional has a global minimizer.
47 citations
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TL;DR: In this article, a stochastic model of electron image detection by slow-scan CCD cameras was developed and employed to investigate the performance of the cameras, taking full account of the channel mixing effect in multi-channel detectors.
47 citations
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TL;DR: An improved PPF-style feature, the spatial pixel pair feature (SPPF), is proposed that better exploits both the spatial/contextual information and spectral information and a flexible multi-stream CNN-based classification framework that is compatible with multiple in-stream sub-network designs.
Abstract: During recent years, convolutional neural network (CNN)-based methods have been widely applied to hyperspectral image (HSI) classification by mostly mining the spectral variabilities. However, the spatial consistency in HSI is rarely discussed except as an extra convolutional channel. Very recently, the development of pixel pair features (PPF) for HSI classification offers a new way of incorporating spatial information. In this paper, we first propose an improved PPF-style feature, the spatial pixel pair feature (SPPF), that better exploits both the spatial/contextual information and spectral information. On top of the new SPPF, we further propose a flexible multi-stream CNN-based classification framework that is compatible with multiple in-stream sub-network designs. The proposed SPPF is different from the original PPF in its paring pixel selection strategy: only pixels immediately adjacent to the central one are eligible, therefore imposing stronger spatial regularization. Additionally, with off-the-shelf classification sub-network designs, the proposed multi-stream, late-fusion CNN-based framework outperforms competing ones without requiring extensive network configuration tuning. Experimental results on three publicly available datasets demonstrate the performance of the proposed SPPF-based HSI classification framework.
46 citations