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Subpixel rendering

About: Subpixel rendering is a research topic. Over the lifetime, 3885 publications have been published within this topic receiving 82789 citations.


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Patent
16 Sep 1996
TL;DR: In this article, a single polarization state of light is transmitted from the backlighting structure to section of the LCD panel where both spatial intensity and spectral filtering of the transmitted polarized light simultaneously occurs on a subpixel basis.
Abstract: An LCD panel employing a novel scheme of systemic light recycling. A single polarization state of light is transmitted from the backlighting structure to section of the LCD panel where both spatial intensity and spectral filtering of the transmitted polarized light simultaneously occurs on a subpixel basis. At each subpixel location, spectral bands of light not transmitted to the display surface during spectral filtering, are reflected without absorption back along the projection axis into the backlighting structure. At a subcomponent level within the LCD panel, spectral components of transmitted polarized light not used at any particular subpixel structure location are effectively reflected either directly or indirectly back into the backlighting structure.

19 citations

Journal ArticleDOI
Huijie Zhao1, Shaoguang Shi1, Hongzhi Jiang1, Ying Zhang1, Zefu Xu1 
TL;DR: A multiplane model (MPM) is proposed with phase fringe to produce dense mark points and a back propagation neural network to obtain subpixel calibration and experiments show that MPM can reduce the back projection error efficiently compared with the pinhole model.
Abstract: A specifically designed imaging system based on an acousto-optic tunable filter (AOTF) can integrate hyperspectral imaging and 3D reconstruction. As a result of the complicated optical structure, the AOTF imaging system deviates from the traditional pinhole model and lens distortion form, causing difficulty to achieve precise camera calibration. The influencing factors leading to the deviation are discussed and a multiplane model (MPM) is proposed with phase fringe to produce dense mark points and a back propagation neural network to obtain subpixel calibration. Experiments show that MPM can reduce the back projection error efficiently compared with the pinhole model. A 3D reconstruction process is conducted based on the calibration result to verify the feasibility of the proposed method.

19 citations

Journal ArticleDOI
TL;DR: A novel spatialresolution enhancement method using fully constrained least squares (FCLS) spectral unmixing and spatial regularization based on modified binary particle swarm optimization is proposed to achieve spatial resolution enhancement in hyperspectral images, without using an additional image with higher spatial resolution.
Abstract: Hyperspectral imaging provides high spectral resolution and thereby improved classification, detection, and recognition capabilities with respect to standard imaging systems. However, hyperspectral images generally have low spatial resolution, varying from a few to tens of meters, resulting from technical limitations such as platform data storing capacity and satellite-to-ground transmission bandwidth. Spectral unmixing provides information on pixels in terms of abundances of pure spectral signatures, without providing spatial distribution at subpixel level. Multisensor image fusion approaches can provide such information but require an additional image with higher spatial resolution that is acquired in similar conditions with the hyperspectral image. In this letter, a novel spatial resolution enhancement method using fully constrained least squares (FCLS) spectral unmixing and spatial regularization based on modified binary particle swarm optimization is proposed to achieve spatial resolution enhancement in hyperspectral images, without using an additional image with higher spatial resolution. The proposed method has a highly parallel nature with respect to its counterparts in the literature and is fit to be adapted to field-programmable gate array architecture.

19 citations

Book ChapterDOI
28 May 2006
TL;DR: This paper develops an FPGA-based data compression technique based on the concept of spectral unmixing that has been implemented on a Xilinx Virtex-IIFPGA formed by several millions of gates, and with high computational power and compact size, which make this reconfigurable device very appealing for onboard, real-time data processing.
Abstract: Hyperspectral data compression is expected to play a crucial role in remote sensing applications. Most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we develop an FPGA-based data compression technique based on the concept of spectral unmixing. It has been implemented on a Xilinx Virtex-II FPGA formed by several millions of gates, and with high computational power and compact size, which make this reconfigurable device very appealing for onboard, real-time data processing.

19 citations

Journal ArticleDOI
TL;DR: In this article, a new method is presented to estimate the abundance of different cover types within individual pixels, and several linear and non-linear multivariate calibration techniques are compared with respect to their ability to establish a relation between pixel values and fractions of ground cover.
Abstract: This paper is concerned with subpixel modelling of land cover estimation. A new method is presented to estimate the abundance of different cover types within individual pixels. Several linear and non-linear multivariate calibration techniques are compared with respect to their ability to establish a relation between pixel values and fractions of ground cover. The method is demonstrated using Landsat-TM imagery and data from Dutch heathlands. By using an optimization procedure for matching field data to image data, a solution was found for the problem of positioning errors in the training set formation.

19 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202387
2022209
2021120
2020179
2019189
2018263