Topic
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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TL;DR: Wang et al. as discussed by the authors proposed a spatially adaptive spectral mixture analysis (SASMA) technique to automatically extract and synthesize the most representative endmembers for SMA through considering both between-class and within-class variations.
150 citations
TL;DR: The contrast and orientation estimation accuracy of several edge operators that have been proposed in the literature is examined both for the noiseless case and in the presence of additive Gaussian noise.
Abstract: The contrast and orientation estimation accuracy of several edge operators that have been proposed in the literature is examined both for the noiseless case and in the presence of additive Gaussian noise. The test image is an ideal step edge that has been sampled with a square-aperture grid. The effects of subpixel translations and rotations of the edge on the performance of the operators are studied. It is shown that the effect of subpixel translations of an edge can generate more error than moderate noise levels. Methods with improved results are presented for Sobel angle estimates and the Nevatia-Babu operator, and theoretical noise performance evaluations are also provided. An edge operator based on two-dimensional spatial moments is presented. All methods are compared according to worst-case and RMS error in an ideal noiseless situation and RMS error under various noise levels. >
148 citations
TL;DR: It is found that the MAP/SMM method is able to reconstruct subpixel information in several principal components of the high-resolution hyperspectral image estimate, while the enhancement for conventional methods, like those based on least squares estimation, is limited primarily to the first principal component.
Abstract: A maximum a posteriori (MAP) estimation method is described for enhancing the spatial resolution of a hyperspectral image using a higher resolution coincident panchromatic image. The approach makes use of a stochastic mixing model (SMM) of the underlying spectral scene content to develop a cost function that simultaneously optimizes the estimated hyperspectral scene relative to the observed hyperspectral and panchromatic imagery, as well as the local statistics of the spectral mixing model. The incorporation of the stochastic mixing model is found to be the key ingredient for reconstructing subpixel spectral information in that it provides the necessary constraints that lead to a well-conditioned linear system of equations for the high-resolution hyperspectral image estimate. Here, the mathematical formulation of the proposed MAP method is described. Also, enhancement results using various hyperspectral image datasets are provided. In general, it is found that the MAP/SMM method is able to reconstruct subpixel information in several principal components of the high-resolution hyperspectral image estimate, while the enhancement for conventional methods, like those based on least squares estimation, is limited primarily to the first principal component (i.e., the intensity component).
148 citations
TL;DR: An analytical approximation of the parametric model of a certain class of characteristic intensity variations in Rohr is developed in such a way that function values can be calculated without numerical integration.
Abstract: The parametric model of a certain class of characteristic intensity variations in Rohr (1990, 1992), which is the superposition of elementary model functions, is employed to identify corners in images. Estimates of the searched model parameters characterizing completely single grey-value structures are determined by a least-squares fit of the model to the observed image intensities applying the minimization method of Levenberg-Marquardt. In particular, we develop an analytical approximation of our model in such a way that function values can be calculated without numerical integration. Assuming the blur of the imaging system to be describable by Gaussian convolution our approach permits subpixel localization of the corner position of the unblurred grey-value structures, that is, to reverse the blur of the imaging system. By fitting our model to the original as well as to the smoothed original-image cues can be obtained for finding out whether the underlying model is an adequate description or not. Results are shown for real image data.
147 citations
Patent•
05 Sep 2008TL;DR: In this paper, a technique for displaying pixels of an image at arbitrary subpixel positions is presented. And interpolated intensity values for the pixels of the image are derived based on the arbitrary sub-pixel location and an intensity distribution or profile.
Abstract: A technique is provided for displaying pixels of an image at arbitrary subpixel positions. In accordance with aspects of this technique, interpolated intensity values for the pixels of the image are derived based on the arbitrary subpixel location and an intensity distribution or profile. Reference to the intensity distribution provides appropriate multipliers for the source image. Based on these multipliers, the image may be rendered at respective physical pixel locations such that the pixel intensities are summed with each rendering, resulting in a destination image having suitable interpolated pixel intensities for the arbitrary subpixel position.
146 citations