The Experts below are selected from a list of 420 Experts worldwide ranked by ideXlab platform
Liangpei Zhang - One of the best experts on this subject based on the ideXlab platform.
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adaptive sparse subpixel mapping with a total variation model for remote sensing imagery
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Ruyi Feng, Yanfei Zhong, Liangpei ZhangAbstract:Subpixel mapping, which is a promising technique based on the assumption of spatial dependence, enhances the spatial resolution of images by dividing a mixed pixel into several Subpixels and assigning each subpixel to a single land-cover class. The traditional subpixel mapping methods usually utilize the fractional abundance images obtained by a spectral unmixing technique as input and consider the spatial correlation information among pixels and Subpixels. However, most of these algorithms treat Subpixels separately and locally while ignoring the rationality of global patterns. In this paper, a novel subpixel mapping model based on sparse representation theory, namely, adaptive sparse subpixel mapping with a total variation model (ASSM-TV), is proposed to explore the possible spatial distribution patterns of Subpixels by considering these Subpixels as an integral patch. In this way, the proposed method can obtain the optimal subpixel mapping result by determining the most appropriate subpixel spatial pattern. However, the number of possible spatial configurations of Subpixels can increase sharply with large-scale factors, and therefore, in ASSM-TV, the subpixel mapping is considered as a sparse representation problem. A preconstructed discrete cosine transform dictionary, which consists of piecewise smooth subpixel patches and textured patches, is utilized to express the original subpixel mapping observation in a sparse representation pattern. The total variation prior model is designed as a spatial regularization constraint to characterize the relationship between a subpixel and its neighboring Subpixels. In addition, a joint maximum a posteriori model is proposed to adaptively select the regularization parameters. Compared with the other traditional and state-of-the-art subpixel mapping approaches, the experimental results using a simulated image, three synthetic hyperspectral remote sensing images, and two real remote sensing images demonstrate that the proposed algorithm can obtain better results, in both visual and quantitative evaluations.
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an adaptive subpixel mapping method based on map model and class determination strategy for hyperspectral remote sensing imagery
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Yanfei Zhong, Liangpei ZhangAbstract:The subpixel mapping technique can specify the spatial distribution of different categories at the subpixel scale by converting the abundance map into a higher resolution image, based on the assumption of spatial dependence. Traditional subpixel mapping algorithms only utilize the low-resolution image obtained by the classification image downsampling and do not consider the spectral unmixing error, which is difficult to account for in real applications. In this paper, to improve the accuracy of the subpixel mapping, an adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery. In AMCDSM, to better simulate a real remote sensing scene, the low-resolution abundance images are obtained by the spectral unmixing method from the downsampled original image or real low-resolution images. The MAP model is extended by considering the spatial prior models (Laplacian, total variation (TV), and bilateral TV) to obtain the high-resolution subpixel distribution map. To avoid the setting of the regularization parameter, an adaptive parameter selection method is designed to acquire the optimal subpixel mapping results. In addition, in AMCDSM, to take into account the spectral unmixing error in real applications, a winner-take-all strategy is proposed to achieve a better subpixel mapping result. The proposed method was tested on simulated, synthetic, and real hyperspectral images, and the experimental results demonstrate that the AMCDSM algorithm outperforms the traditional subpixel mapping methods and provides a simple and efficient algorithm to regularize the ill-posed subpixel mapping problem.
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adaptive subpixel mapping based on a multiagent system for remote sensing imagery
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Yanfei Zhong, Liangpei ZhangAbstract:The existence of mixed pixels is a major problem in remote-sensing image classification. Although the soft classification and spectral unmixing techniques can obtain an abundance of different classes in a pixel to solve the mixed pixel problem, the subpixel spatial attribution of the pixel will still be unknown. The subpixel mapping technique can effectively solve this problem by providing a fine-resolution map of class labels from coarser spectrally unmixed fraction images. However, most traditional subpixel mapping algorithms treat all mixed pixels as an identical type, either boundary-mixed pixel or linear subpixel, leading to incomplete and inaccurate results. To improve the subpixel mapping accuracy, this paper proposes an adaptive subpixel mapping framework based on a multiagent system for remote-sensing imagery. In the proposed multiagent subpixel mapping framework, three kinds of agents, namely, feature detection agents, subpixel mapping agents and decision agents, are designed to solve the subpixel mapping problem. Experiments with artificial images and synthetic remote-sensing images were performed to evaluate the performance of the proposed subpixel mapping algorithm in comparison with the hard classification method and other subpixel mapping algorithms: subpixel mapping based on a back-propagation neural network and the spatial attraction model. The experimental results indicate that the proposed algorithm outperforms the other two subpixel mapping algorithms in reconstructing the different structures in mixed pixels.
Lu Fang - One of the best experts on this subject based on the ideXlab platform.
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Antialiasing Filter Design for Subpixel Downsampling via Frequency-Domain Analysis
2013Co-Authors: Lu Fang, Ketan Tang, Aggelos K. KatsaggelosAbstract:Abstract—In this paper, we are concerned with image downsampling using subpixel techniques to achieve superior sharpness for small liquid crystal displays (LCDs). Such a problem exists when a high-resolution image or video is to be displayed on low-resolution display terminals. Limited by the low-resolution display, we have to shrink the image. Signal-processing theory tells us that optimal decimation requires low-pass filtering with a suitable cutoff frequency, followed by downsampling. In doing so, we need to remove many useful image details causing blurring. Subpixel-based downsampling, taking advantage of the fact that each pixel on a color LCD is actually composed of individual red, green, and blue subpixel stripes, can provide apparent higher resolution. In this paper, we use frequency-domain analysis to explain what happens in subpixel-based downsampling and why it is possible to achieve a higher apparent resolution. According to our frequency-domain analysis and observation, the cutoff frequency of the low-pass filter for subpixel-based decimation can be effectively extended beyond the Nyquist frequency using a novel antialiasing filter. Applying the proposed filters to two existing subpixel downsampling schemes called direct subpixel-based downsampling (DSD) and diagonal DSD (DDSD), we obtain two improved schemes, i.e., DSD based on frequency-domain analysis (DSD-FA) and DDSD based on frequency-domain analysis (DDSD-FA). Experimental results verify that the proposed DSD-FA and DDSD-FA can provide superior results, compared with existing subpixel or pixel-based downsampling methods. Index Terms—Downsampling, frequency analysis, subpixel rendering. I
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ADAPTIVE JOINT DEMOSAICING AND SUBPIXEL-BASED DOWN-SAMPLING FOR BAYER IMAGE
2013Co-Authors: Lu Fang, Aggelos K. KatsaggelosAbstract:A digital camera provided with a Bayer pattern single sensor needs color interpolation to reconstruct a full color image. To show high resolution image on a lower resolution display, it must then be downsampled. These two steps influence each other, i.e., the color artifacts introduced in demosaicing may be magnified in subsequent down-sampling process and vice versa. Thanks to the fact that LCD displays are actually composed of separable Subpixels, which can be individually addressed to achieve a higher effective apparent resolution. This paper presents an Adaptive Joint Demosaicing and Subpixel-based Down-sampling scheme (AJDSD) for singlesensor camera image, where the subpixel-based down-sampling is adaptively and directly applied in Bayer domain, without the process of demosaicing. Simulation results demonstrate that when compared with conventional “demosaicing-first and downsamplinglater” methods, AJDSD achieves superior performance improvement in terms of computational complexity. As for visual quality, AJDSD is more effective in preserving high frequency details, leading to much sharper and clearer results. 1
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antialiasing filter design for subpixel downsampling via frequency domain analysis
IEEE Transactions on Image Processing, 2012Co-Authors: Lu Fang, Oscar C. Au, Ketan TangAbstract:In this paper, we are concerned with image downsampling using subpixel techniques to achieve superior sharpness for small liquid crystal displays (LCDs). Such a problem exists when a high-resolution image or video is to be displayed on low-resolution display terminals. Limited by the low-resolution display, we have to shrink the image. Signal-processing theory tells us that optimal decimation requires low-pass filtering with a suitable cutoff frequency, followed by downsampling. In doing so, we need to remove many useful image details causing blurring. Subpixel-based downsampling, taking advantage of the fact that each pixel on a color LCD is actually composed of individual red, green, and blue subpixel stripes, can provide apparent higher resolution. In this paper, we use frequency-domain analysis to explain what happens in subpixel-based downsampling and why it is possible to achieve a higher apparent resolution. According to our frequency-domain analysis and observation, the cutoff frequency of the low-pass filter for subpixel-based decimation can be effectively extended beyond the Nyquist frequency using a novel antialiasing filter. Applying the proposed filters to two existing subpixel downsampling schemes called direct subpixel-based downsampling (DSD) and diagonal DSD (DDSD), we obtain two improved schemes, i.e., DSD based on frequency-domain analysis (DSD-FA) and DDSD based on frequency-domain analysis (DDSD-FA). Experimental results verify that the proposed DSD-FA and DDSD-FA can provide superior results, compared with existing subpixel or pixel-based downsampling methods.
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Novel 2-D MMSE subpixelbased image downsampling
2012Co-Authors: Lu Fang, Ketan Tang, Xing Wen, Hanli WangAbstract:Abstract—Subpixel-based down-sampling is a method that can potentially improve apparent resolution of a down-scaled image on LCD by controlling individual Subpixels rather than pixels. However, the increased luminance resolution comes at price of chrominance distortion. A major challenge is to suppress color fringing artifacts while maintaining sharpness. We propose a new subpixel-based down-sampling pattern called diagonal direct subpixel-based down-sampling (DDSD) for which we design a 2-D image reconstruction model. Then, we formulate subpixel-based down-sampling as a MMSE problem and derive the optimal solution called minimum mean square error for subpixel-based down-sampling (MMSE-SD). Unfortunately, straightfor-ward implementation of MMSE-SD is computational intensive. We thus prove that the solution is equivalent to a 2-D linear filter followed by DDSD, which is much simpler. We further reduce computational complexity using a small k × k filter to approximate the much larger MMSE-SD filter. To compare the performances of pixel and subpixel-based down-sampling methods, we propose two novel objective measures: normalized l1 high frequency energy for apparent luminance sharpness and PSNRU(V) for chrominance distortion. Simulation results show that both MMSE-SD and MMSE-SD(k) can give sharper images compared with conventional down-sampling methods, with little color fringing artifacts. Index Terms—Color fringing, image down-sampling, subpixel rendering. I
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Digital image and video processing using subpixel rendering
2011Co-Authors: Lu FangAbstract:Subpixel rendering techniques originate from the problem of monochromatic font rendering on LCDs. It takes advantage of the fact that a single pixel on a color LCD display consists of several primary colors, typically three colored stripes (Subpixels) ordered red, green, and blue (RGB). Researchers found that, by controlling the subpixel values of neighboring pixels, it is possible to micro-shift the apparent position of a line to gives greater details of text. In this thesis, we address the problem of color image and video processing using subpixel rendering techniques to achieve superior sharpness for small LCD displays by controlling individual Subpixels rather than pixels. However, the increased luminance resolution often comes at the price of chrominance distortion. A major challenge is to suppress color fringing artifacts while maintaining sharpness. First, we discuss subpixel rendering for down-sampling problem, such a problem exists when a high resolution image or video is to be displayed on low resolution display terminals (i.e., Mobile). We start by formulating subpixel-based down-sampling as different optimization problems based on different reconstruction models in spatial domain: MMDE (Min-Max Directional Error) and MMSESD (MMSE for subpixel-based down-sampling). Simulation results show our proposed subpixel-based methods can give much sharper images compared with the conventional pixel-based methods, without noticeable color fringing artifacts. To better understand what happens in subpixel-based algorithm, we further propose novel frequency domain analysis approach to explain why it is possible to achieve a higher apparent resolution using subpixel techniques. Our theoretical analysis shows that the cut-off frequency of the low-pass filter for subpixel-based decimation can be effectively extended beyond the Nyquist frequency using novel anti-aliasing filters. We also investigate the problem of low bit-rate JPEG compression. Since JPEG introduces severe blocking artifacts under low bit-rate, researchers have proposed a down-sampled image when compressed and later interpolated scheme, which provides superior performance than high resolution image compressed directly. However, pixel-based down-sampling loses high frequency details, resulting in blurring reconstructed image. We thus proposed subpixel-based low bit-rate JPEG compression algorithm without changing decoding system, which can provide about 3dB higher PSNR than pixel-based low bit-rate JPEG compression scheme, under the same bit-rates. Finally, we address the problem of displaying high resolution one-color Bayer image on low-resolution LCD screen of portable devices, which requires demosaicking followed by down-sampling. However, these two steps require high computational complexity, and the color artifacts introduced in demosaicking will be magnified in down-sampling. We thus propose joint demosaicking and subpixel-based down-sampling algorithm by directly performing subpixel-based down-sampling in Bayer domain without demosaicking, resulting in greatly reduced computational complexity and sharper down-sampled images
Sukju Kang - One of the best experts on this subject based on the ideXlab platform.
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adaptive weight allocation based subpixel rendering algorithm
IEEE Transactions on Circuits and Systems for Video Technology, 2014Co-Authors: Sukju KangAbstract:In this letter, a new approach is presented for adaptive weight allocation-based subpixel rendering in organic light-emitting diode displays. Subpixel rendering is used to enhance the apparent resolution without changing a pixel structure. Existing methods have blurring and color fringing artifact after subpixel rendering. The proposed method, on the other hand, dynamically controls weights of current and neighboring pixels based on color difference. Thus, it preserves the image quality, while increasing the resolution. In experiments, the proposed subpixel rendering improved luminance sharpness by up to 0.043, when compared with the benchmark methods. For chrominance blending, the peak signal-to-noise ratio of the proposed method was up to 6.192 dB higher than those of benchmark methods.
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color difference based subpixel rendering for matrix displays
IEEE\ OSA Journal of Display Technology, 2013Co-Authors: Sukju KangAbstract:In this paper, we present a new subpixel rendering algorithm considering adaptively weighting factors of the current and neighboring Subpixels for matrix displays. The proposed method divides gray levels into several bins computing the absolute color difference between the current subpixel and neighboring Subpixels. Then, it adaptively assigns weighting factors to the current and neighboring Subpixels. In the experiments using test images, the proposed subpixel rendering algorithm improved the average luminance sharpness by up to 0.124, when compared to benchmarks methods. For the chrominance blending, the average peak signal-to-noise ratio (PSNR) of the proposed method was up to 1.415 dB higher than those of the benchmarks methods.
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luminance difference based adaptive subpixel rendering algorithm for matrix displays
International Conference on Consumer Electronics, 2012Co-Authors: Sukju Kang, Seonggyun Kim, Youngwon SongAbstract:This paper presents an adaptive subpixel rendering algorithm that uses the luminance difference between the current subpixel and neighboring Subpixels. The proposed method reduces both the color fringing artifact and blurring, thereby enhancing greatly the image quality.
Peter M. Atkinson - One of the best experts on this subject based on the ideXlab platform.
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Spatiotemporal Subpixel Mapping of Time-Series Images
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Qunming Wang, Wenzhong Shi, Peter M. AtkinsonAbstract:Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into Subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs.
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superresolution mapping using a hopfield neural network with lidar data
IEEE Geoscience and Remote Sensing Letters, 2005Co-Authors: M Q Nguyen, Peter M. Atkinson, Hugh G LewisAbstract:Superresolution mapping is a set of techniques to obtain a subpixel map from land cover proportion images produced by soft classification. Together with the information from the land cover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. This research aims to use the elevation data from light detection and ranging (lidar) as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). A new height function was added to the energy function of the HNN for superresolution mapping. The value of the height function was calculated for each subpixel of a certain class based on the Gaussian distribution. A set of simulated data was used to test the new technique. The results suggest that 0.8-m spatial resolution digital surface models can be combined with optical data at 4-m spatial resolution for superresolution mapping.
Yanfei Zhong - One of the best experts on this subject based on the ideXlab platform.
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adaptive sparse subpixel mapping with a total variation model for remote sensing imagery
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Ruyi Feng, Yanfei Zhong, Liangpei ZhangAbstract:Subpixel mapping, which is a promising technique based on the assumption of spatial dependence, enhances the spatial resolution of images by dividing a mixed pixel into several Subpixels and assigning each subpixel to a single land-cover class. The traditional subpixel mapping methods usually utilize the fractional abundance images obtained by a spectral unmixing technique as input and consider the spatial correlation information among pixels and Subpixels. However, most of these algorithms treat Subpixels separately and locally while ignoring the rationality of global patterns. In this paper, a novel subpixel mapping model based on sparse representation theory, namely, adaptive sparse subpixel mapping with a total variation model (ASSM-TV), is proposed to explore the possible spatial distribution patterns of Subpixels by considering these Subpixels as an integral patch. In this way, the proposed method can obtain the optimal subpixel mapping result by determining the most appropriate subpixel spatial pattern. However, the number of possible spatial configurations of Subpixels can increase sharply with large-scale factors, and therefore, in ASSM-TV, the subpixel mapping is considered as a sparse representation problem. A preconstructed discrete cosine transform dictionary, which consists of piecewise smooth subpixel patches and textured patches, is utilized to express the original subpixel mapping observation in a sparse representation pattern. The total variation prior model is designed as a spatial regularization constraint to characterize the relationship between a subpixel and its neighboring Subpixels. In addition, a joint maximum a posteriori model is proposed to adaptively select the regularization parameters. Compared with the other traditional and state-of-the-art subpixel mapping approaches, the experimental results using a simulated image, three synthetic hyperspectral remote sensing images, and two real remote sensing images demonstrate that the proposed algorithm can obtain better results, in both visual and quantitative evaluations.
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an adaptive subpixel mapping method based on map model and class determination strategy for hyperspectral remote sensing imagery
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Yanfei Zhong, Liangpei ZhangAbstract:The subpixel mapping technique can specify the spatial distribution of different categories at the subpixel scale by converting the abundance map into a higher resolution image, based on the assumption of spatial dependence. Traditional subpixel mapping algorithms only utilize the low-resolution image obtained by the classification image downsampling and do not consider the spectral unmixing error, which is difficult to account for in real applications. In this paper, to improve the accuracy of the subpixel mapping, an adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery. In AMCDSM, to better simulate a real remote sensing scene, the low-resolution abundance images are obtained by the spectral unmixing method from the downsampled original image or real low-resolution images. The MAP model is extended by considering the spatial prior models (Laplacian, total variation (TV), and bilateral TV) to obtain the high-resolution subpixel distribution map. To avoid the setting of the regularization parameter, an adaptive parameter selection method is designed to acquire the optimal subpixel mapping results. In addition, in AMCDSM, to take into account the spectral unmixing error in real applications, a winner-take-all strategy is proposed to achieve a better subpixel mapping result. The proposed method was tested on simulated, synthetic, and real hyperspectral images, and the experimental results demonstrate that the AMCDSM algorithm outperforms the traditional subpixel mapping methods and provides a simple and efficient algorithm to regularize the ill-posed subpixel mapping problem.
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adaptive subpixel mapping based on a multiagent system for remote sensing imagery
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Yanfei Zhong, Liangpei ZhangAbstract:The existence of mixed pixels is a major problem in remote-sensing image classification. Although the soft classification and spectral unmixing techniques can obtain an abundance of different classes in a pixel to solve the mixed pixel problem, the subpixel spatial attribution of the pixel will still be unknown. The subpixel mapping technique can effectively solve this problem by providing a fine-resolution map of class labels from coarser spectrally unmixed fraction images. However, most traditional subpixel mapping algorithms treat all mixed pixels as an identical type, either boundary-mixed pixel or linear subpixel, leading to incomplete and inaccurate results. To improve the subpixel mapping accuracy, this paper proposes an adaptive subpixel mapping framework based on a multiagent system for remote-sensing imagery. In the proposed multiagent subpixel mapping framework, three kinds of agents, namely, feature detection agents, subpixel mapping agents and decision agents, are designed to solve the subpixel mapping problem. Experiments with artificial images and synthetic remote-sensing images were performed to evaluate the performance of the proposed subpixel mapping algorithm in comparison with the hard classification method and other subpixel mapping algorithms: subpixel mapping based on a back-propagation neural network and the spatial attraction model. The experimental results indicate that the proposed algorithm outperforms the other two subpixel mapping algorithms in reconstructing the different structures in mixed pixels.
